diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..0f89448
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,8 @@
+.idea
+.coverage
+htmlcov/
+MOT17Labels/
+*.zip
+outputs/
+MOT17/
+venv/
diff --git a/README.md b/README.md
index 91bc3ec..5e3ecee 100644
--- a/README.md
+++ b/README.md
@@ -16,22 +16,19 @@ See the [arXiv preprint](http://arxiv.org/abs/2103.04147) for more information.
 <img align="center" width="48%" src="https://github.com/mhnasseri/sort_oh/blob/main/Video_MOT17-09-SDP.gif">
 </p>
 
-## Dependencies
-
-The code is compatible with Python 3. The following dependencies are
-needed to run the tracker:
-
-* NumPy
-* sklearn
-* OpenCV
-* filterpy
-
 ## Installation
 
 First, clone the repository:
 ```
 git clone https://github.com/mhnasseri/sort_oh.git
 ```
+
+Install the dependencies using
+
+```
+pip install -r requirements.txt
+```
+
 Then, download the MOT17 dataset from [here](https://motchallenge.net/data/MOT17/).
 
 In order to results on MOT16 be comparable with the results of SORT and DeepSORT algorithms, the private detection taken from the following paper:
diff --git a/libs/association.py b/libs/association.py
index 4331412..80c3464 100644
--- a/libs/association.py
+++ b/libs/association.py
@@ -1,37 +1,58 @@
 import numpy as np
 from scipy.optimize import linear_sum_assignment
+
+from . import utils
 from . import calculate_cost
 from . import visualization
 
 
-def associate_detections_to_trackers(mot_tracker, detections, trackers, groundtruths, average_area, iou_threshold=0.3):
-    """
-  Assigns detections to tracked object (both represented as bounding boxes)
-  Returns 5 lists of matches, unmatched_detections, unmatched_trackers, occluded_trackers and unmatched ground truths
-  """
-    if len(trackers) == 0 or len(detections) == 0:
-        return np.empty((0, 2), dtype=int), np.arange(len(detections)), np.empty((0, 1), dtype=int), np.empty((0, 1), dtype=int), np.empty((0, 1), dtype=int)
+def __array_of_ndarray_tolist(l):
+    return utils.list_of_ndarray_tolist(l)
 
-    # assign only according to iou
-    # calculate intersection over union cost
-    iou_matrix = calculate_cost.cal_iou(detections, trackers)
-    ios_matrix = calculate_cost.cal_ios_matrix(trackers)
 
-    matched_indices = linear_sum_assignment(-iou_matrix)
-    matched_indices = np.asarray(matched_indices)
-    matched_indices = np.transpose(matched_indices)     # first column: detection indexes, second column: object indexes
+def __kalmantrackers_list_to_dict(kalman_trackers):
+    return list(map(lambda x: {
+        "time_since_observed": x.time_since_observed,
+        "confidence": x.confidence if isinstance(x.confidence, float) or isinstance(x.confidence, int) else float(
+            x.confidence.tolist()[0]),
+        "age": x.age,
+    }, kalman_trackers))
+
+
+def __tracker_to_dict(tracker):
+    return {
+        "trackers": __kalmantrackers_list_to_dict(tracker.trackers),
+        "frame_count": tracker.frame_count,
+        "min_hits": tracker.min_hits,
+        "conf_trgt": tracker.conf_trgt,
+        "conf_objt": tracker.conf_objt,
+        "unmatched_before": __array_of_ndarray_tolist(tracker.unmatched_before),
+    }
+
 
+def _unmatched_detections(detections, matched_indices):
     unmatched_detections = []
     for d, det in enumerate(detections):
         if d not in matched_indices[:, 0]:
             unmatched_detections.append(d)
 
+    return unmatched_detections
+
+
+def _unmatched_trackers(trackers, matched_indices):
     unmatched_trackers = []
     for t, trk in enumerate(trackers):
         if t not in matched_indices[:, 1]:
             unmatched_trackers.append(t)
 
-    # filter out matched with low IOU
+    return unmatched_trackers
+
+
+def _filter_low_iou(matched_indices, iou_matrix, iou_threshold, unmatched_detections, unmatched_trackers,
+                    kalman_trackers):
+    """
+    WARNING: This writes to kalman_trackers, unmatched_detections, unmatched_trackers
+    """
     matches = []
     for m in matched_indices:
         if iou_matrix[m[0], m[1]] < iou_threshold:
@@ -39,7 +60,127 @@ def associate_detections_to_trackers(mot_tracker, detections, trackers, groundtr
             unmatched_trackers.append(m[1])
         else:
             matches.append(m.reshape(1, 2))
-            mot_tracker.trackers[m[1]].time_since_observed = 0
+            kalman_trackers[m[1]].time_since_observed = 0
+
+    return matches
+
+
+def _clean_up_to_delete(matches_ext_com, unmatched_detections, unmatched_trackers, matches, unmatched_before):
+    """
+    WARNING: This writes to unmatched_before
+    """
+    to_del_und = []
+    to_del_unt = []
+    to_del_undb = []
+    matches_ext = np.empty((0, 2), dtype=int)
+    if len(matches_ext_com) > 0:
+        matches_ext_com = np.concatenate(matches_ext_com, axis=0)
+        for m in matches_ext_com:
+            new = np.array([[unmatched_detections[m[1]], unmatched_trackers[m[0]]]])
+            matches_ext = np.append(matches_ext, new, axis=0)
+            to_del_unt.append(m[0])
+            to_del_und.append(m[1])
+            to_del_undb.append(m[2])
+    matches = np.concatenate((matches, matches_ext))
+    if len(to_del_unt) > 0:
+        to_del_unt = np.array(to_del_unt)
+        to_del_unt = np.sort(to_del_unt)
+        for i in reversed(to_del_unt):
+            unmatched_trackers = np.delete(unmatched_trackers, i, 0)
+    if len(to_del_und) > 0:
+        to_del_und = np.array(to_del_und)
+        to_del_und = np.sort(to_del_und)
+        for i in reversed(to_del_und):
+            unmatched_detections = np.delete(unmatched_detections, i, 0)
+    if len(to_del_undb) > 0:
+        to_del_undb = np.array(to_del_undb)
+        to_del_undb = np.sort(to_del_undb)
+        for i in reversed(to_del_undb):
+            unmatched_before.pop(i)
+
+    return matches, unmatched_trackers, unmatched_detections
+
+
+def _occluded_trackers(frame_count, min_hits, ios_matrix, unmatched_trackers, trackers, kalman_trackers, average_area,
+                       conf_trgt, conf_objt):
+    """
+    WARNING: This writes to kalman_trackers
+    """
+    occluded_trackers = []
+    if frame_count > min_hits:
+        trks_occlusion = np.amax(ios_matrix, axis=0)
+        unm_trks = unmatched_trackers
+        unmatched_trackers = []
+        for ut in unm_trks:
+            ut_area = (trackers[ut, 3] - trackers[ut, 1]) * (trackers[ut, 2] - trackers[ut, 0])
+            kalman_trackers[ut].time_since_observed += 1
+            kalman_trackers[ut].confidence = min(1, kalman_trackers[ut].age / (
+                    kalman_trackers[ut].time_since_observed * 10) * (ut_area / average_area))
+            if trks_occlusion[ut] > 0.3 and kalman_trackers[ut].confidence > conf_trgt:
+                # if trks_occlusion[ut] > 0.3 and ut_area > 0.7 * average_area and mot_tracker.trackers[ut].age > 5:
+                occluded_trackers.append(ut)
+            elif kalman_trackers[ut].confidence > conf_objt:
+                # elif mot_tracker.trackers[ut].age > (mot_tracker.trackers[ut].time_since_observed * 10 + 10) and mot_tracker.trackers[ut].time_since_observed < 5:
+                occluded_trackers.append(ut)
+            else:
+                unmatched_trackers.append(ut)
+
+    return occluded_trackers, unmatched_trackers
+
+
+def _build_iou_matrix_ext(unmatched_trackers, unmatched_detections, detections, trackers, kalman_trackers):
+    iou_matrix_ext = np.zeros((len(unmatched_trackers), len(unmatched_detections)), dtype=np.float32)
+    for ud in range(len(unmatched_detections)):
+        for ut in range(len(unmatched_trackers)):
+            param_x = np.minimum(1.2, (kalman_trackers[unmatched_trackers[ut]].time_since_observed + 1) * 0.3)
+            param_y = np.minimum(0.5, (kalman_trackers[unmatched_trackers[ut]].time_since_observed + 1) * 0.1)
+            iou_matrix_ext[ut, ud] = calculate_cost.iou_ext_sep(detections[unmatched_detections[ud]],
+                                                                trackers[unmatched_trackers[ut]],
+                                                                param_x,
+                                                                param_y)
+    return iou_matrix_ext
+
+
+def _filter_low_iou_low_area(matched_indices_ext, matched_indices, iou_matrix_ext, iou_threshold, iou_matrix):
+    matches_ext_com = []
+    del_ind = np.empty((0, 3), dtype=int)
+    for m in matched_indices_ext:
+        ind = matched_indices[np.where(matched_indices[:, 0] == m[1]), 1]
+        if ind.size:
+            if (iou_matrix_ext[m[0], m[1]] >= iou_threshold) and (iou_matrix[m[1], ind] >= iou_threshold):
+                matches_ext_com.append(np.concatenate((m.reshape(1, 2), ind), axis=1))
+                # remove matched detections from unmatched arrays
+                del_ind = np.concatenate((del_ind, np.array([m[0], m[1], ind.item(0)]).reshape(1, 3)))
+
+    return matches_ext_com, del_ind
+
+
+def associate_detections_to_trackers(mot_tracker, detections, trackers, groundtruths, average_area,
+                                     iou_threshold=0.3, ):
+    """
+    Assigns detections to tracked object (both represented as bounding boxes)
+    Returns 5 lists of matches, unmatched_detections, unmatched_trackers, occluded_trackers and unmatched ground truths
+    """
+    if len(trackers) == 0 or len(detections) == 0:
+        return np.empty((0, 2), dtype=int), \
+               np.arange(len(detections)), \
+               np.empty((0, 1), dtype=int),\
+               np.empty((0, 1), dtype=int),\
+               np.empty((0, 1), dtype=int)
+
+    # assign only according to iou
+    # calculate intersection over union cost
+    iou_matrix = calculate_cost.cal_iou(detections, trackers)
+    ios_matrix = calculate_cost.cal_ios_matrix(trackers)
+
+    matched_indices = linear_sum_assignment(-iou_matrix)
+    matched_indices = np.asarray(matched_indices)
+    matched_indices = np.transpose(matched_indices)  # first column: detection indexes, second column: object indexes
+
+    unmatched_detections = _unmatched_detections(detections, matched_indices)
+    unmatched_trackers = _unmatched_trackers(trackers, matched_indices)
+    matches = _filter_low_iou(matched_indices, iou_matrix, iou_threshold, unmatched_detections, unmatched_trackers,
+                              mot_tracker.trackers)
 
     unmatched_detections = np.array(unmatched_detections)
     unmatched_trackers = np.array(unmatched_trackers)
@@ -53,83 +194,30 @@ def associate_detections_to_trackers(mot_tracker, detections, trackers, groundtr
             unm_dets = []
             for ud in unmatched_detections:
                 unm_dets.append(detections[ud])
+
             iou_matrix = calculate_cost.cal_iou(unm_dets, mot_tracker.unmatched_before)
             matched_indices = linear_sum_assignment(-iou_matrix)
             matched_indices = np.asarray(matched_indices)
             matched_indices = np.transpose(matched_indices)
 
-            iou_matrix_ext = np.zeros((len(unmatched_trackers), len(unmatched_detections)), dtype=np.float32)
-            for ud in range(len(unmatched_detections)):
-                for ut in range(len(unmatched_trackers)):
-                    iou_matrix_ext[ut, ud] = calculate_cost.iou_ext_sep(detections[unmatched_detections[ud]],
-                                                                        trackers[unmatched_trackers[ut]],
-                                                                        np.minimum(1.2, (mot_tracker.trackers[
-                                                                                             unmatched_trackers[
-                                                                                                 ut]].time_since_observed + 1) * 0.3),
-                                                                        np.minimum(0.5, (mot_tracker.trackers[
-                                                                                             unmatched_trackers[
-                                                                                                 ut]].time_since_observed + 1) * 0.1))
+            iou_matrix_ext = _build_iou_matrix_ext(unmatched_trackers, unmatched_detections, detections, trackers,
+                                                   mot_tracker.trackers)
             matched_indices_ext = linear_sum_assignment(-iou_matrix_ext)
             matched_indices_ext = np.asarray(matched_indices_ext)
             matched_indices_ext = np.transpose(matched_indices_ext)
 
-            # filter out matched with low IOU and low area
-            matches_ext_com = []
-            del_ind = np.empty((0, 3), dtype=int)
-            for m in matched_indices_ext:
-                ind = matched_indices[np.where(matched_indices[:, 0] == m[1]), 1]
-                if ind.size:
-                    if (iou_matrix_ext[m[0], m[1]] >= iou_threshold) and (iou_matrix[m[1], ind] >= iou_threshold):
-                        matches_ext_com.append(np.concatenate((m.reshape(1, 2), ind), axis=1))
-                        # remove matched detections from unmatched arrays
-                        del_ind = np.concatenate((del_ind, np.array([m[0], m[1], ind.item(0)]).reshape(1, 3)))
-
-            to_del_und = []
-            to_del_unt = []
-            to_del_undb = []
-            matches_ext = np.empty((0, 2), dtype=int)
-            if len(matches_ext_com) > 0:
-                matches_ext_com = np.concatenate(matches_ext_com, axis=0)
-                for m in matches_ext_com:
-                    new = np.array([[unmatched_detections[m[1]], unmatched_trackers[m[0]]]])
-                    matches_ext = np.append(matches_ext, new, axis=0)
-                    to_del_unt.append(m[0])
-                    to_del_und.append(m[1])
-                    to_del_undb.append(m[2])
-            matches = np.concatenate((matches, matches_ext))
-            if len(to_del_unt) > 0:
-                to_del_unt = np.array(to_del_unt)
-                to_del_unt = np.sort(to_del_unt)
-                for i in reversed(to_del_unt):
-                    unmatched_trackers = np.delete(unmatched_trackers, i, 0)
-            if len(to_del_und) > 0:
-                to_del_und = np.array(to_del_und)
-                to_del_und = np.sort(to_del_und)
-                for i in reversed(to_del_und):
-                    unmatched_detections = np.delete(unmatched_detections, i, 0)
-            if len(to_del_undb) > 0:
-                to_del_undb = np.array(to_del_undb)
-                to_del_undb = np.sort(to_del_undb)
-                for i in reversed(to_del_undb):
-                    mot_tracker.unmatched_before.pop(i)
+            matches_ext_com, del_ind = _filter_low_iou_low_area(matched_indices_ext, matched_indices, iou_matrix_ext,
+                                                                iou_threshold, iou_matrix)
 
-    occluded_trackers = []
-    if mot_tracker.frame_count > mot_tracker.min_hits:
-        trks_occlusion = np.amax(ios_matrix, axis=0)
-        unm_trks = unmatched_trackers
-        unmatched_trackers = []
-        for ut in unm_trks:
-            ut_area = (trackers[ut, 3] - trackers[ut, 1])*(trackers[ut, 2] - trackers[ut, 0])
-            mot_tracker.trackers[ut].time_since_observed += 1
-            mot_tracker.trackers[ut].confidence = min(1, mot_tracker.trackers[ut].age/(mot_tracker.trackers[ut].time_since_observed*10)*(ut_area/average_area))
-            if trks_occlusion[ut] > 0.3 and mot_tracker.trackers[ut].confidence > mot_tracker.conf_trgt:
-            # if trks_occlusion[ut] > 0.3 and ut_area > 0.7 * average_area and mot_tracker.trackers[ut].age > 5:
-                occluded_trackers.append(ut)
-            elif mot_tracker.trackers[ut].confidence > mot_tracker.conf_objt:
-            # elif mot_tracker.trackers[ut].age > (mot_tracker.trackers[ut].time_since_observed * 10 + 10) and mot_tracker.trackers[ut].time_since_observed < 5:
-                occluded_trackers.append(ut)
-            else:
-                unmatched_trackers.append(ut)
+            matches, unmatched_trackers, unmatched_detections = _clean_up_to_delete(matches_ext_com,
+                                                                                    unmatched_detections,
+                                                                                    unmatched_trackers, matches,
+                                                                                    mot_tracker.unmatched_before)
+
+    occluded_trackers, unmatched_trackers = _occluded_trackers(mot_tracker.frame_count, mot_tracker.min_hits,
+                                                               ios_matrix, unmatched_trackers, trackers,
+                                                               mot_tracker.trackers, average_area,
+                                                               mot_tracker.conf_trgt, mot_tracker.conf_objt)
 
     # find unmatched ground truths
     unmatched_groundtruths = []
@@ -147,40 +235,41 @@ def associate_detections_to_trackers(mot_tracker, detections, trackers, groundtr
             if g not in matched_indices_1[:, 0]:
                 unmatched_groundtruths.append(g)
 
-    return matches, unmatched_detections, unmatched_trackers, np.array(occluded_trackers), np.array(unmatched_groundtruths)
+    return matches, unmatched_detections, unmatched_trackers, np.array(occluded_trackers), np.array(
+        unmatched_groundtruths)
 
 
-def find_new_trackers(detections, detections_before, iou_threshold=0.3):
-    """
-  Detect new trackers from unmateched detection in current frame and before frame (both represented as bounding boxes)
-  Returns list of new trackers
-  """
-    if len(detections) == 0 or len(detections_before) == 0:
-        return np.empty((0, 2), dtype=int)
-    iou_matrix = calculate_cost.cal_iou(detections, detections_before)
-    # first column: detection indexes, second column: object indexes
-    matched_indices = linear_sum_assignment(-iou_matrix)
-    matched_indices = np.asarray(matched_indices)
-    matched_indices = np.transpose(matched_indices)
-
-    # filter out matched with low IOU
-    matches = []
-    for m in matched_indices:
-        if iou_matrix[m[0], m[1]] >= iou_threshold:
-            matches.append(m.reshape(1, 2))
-    if len(matches) == 0:
-        matches = np.empty((0, 2), dtype=int)
-    else:
-        matches = np.concatenate(matches, axis=0)
-
-    return matches
+# def find_new_trackers(detections, detections_before, iou_threshold=0.3):
+#     """
+#   Detect new trackers from unmateched detection in current frame and before frame (both represented as bounding boxes)
+#   Returns list of new trackers
+#   """
+#     if len(detections) == 0 or len(detections_before) == 0:
+#         return np.empty((0, 2), dtype=int)
+#     iou_matrix = calculate_cost.cal_iou(detections, detections_before)
+#     # first column: detection indexes, second column: object indexes
+#     matched_indices = linear_sum_assignment(-iou_matrix)
+#     matched_indices = np.asarray(matched_indices)
+#     matched_indices = np.transpose(matched_indices)
+#
+#     # filter out matched with low IOU
+#     matches = []
+#     for m in matched_indices:
+#         if iou_matrix[m[0], m[1]] >= iou_threshold:
+#             matches.append(m.reshape(1, 2))
+#     if len(matches) == 0:
+#         matches = np.empty((0, 2), dtype=int)
+#     else:
+#         matches = np.concatenate(matches, axis=0)
+#
+#     return matches
 
 
 def find_new_trackers_2(detections, detections_before, detections_before_before, average_area, iou_threshold=0.3):
     """
-  Detect new trackers from unmateched detection in current frame and before frame (both represented as bounding boxes)
-  Returns list of new trackers
-  """
+    Detect new trackers from unmateched detection in current frame and before frame (both represented as bounding boxes)
+    Returns list of new trackers
+    """
     if len(detections) == 0 or len(detections_before) == 0 or len(detections_before_before) == 0:
         return np.empty((0, 2), dtype=int)
     iou_matrix = calculate_cost.cal_iou(detections, detections_before)
@@ -212,7 +301,3 @@ def find_new_trackers_2(detections, detections_before, detections_before_before,
         matches = np.concatenate(matches, axis=0)
 
     return matches, del_ind
-
-
-
-
diff --git a/libs/calculate_cost.py b/libs/calculate_cost.py
index 60f5db5..146956e 100644
--- a/libs/calculate_cost.py
+++ b/libs/calculate_cost.py
@@ -2,11 +2,11 @@
 import numpy as np
 
 
-@jit
+# @jit
 def iou(bb_det, bb_trk):
     """
-  Computes IOU (Intersection Over Union) between two bounding boxes in the form [x1,y1,x2,y2]
-  """
+    Computes IOU (Intersection Over Union) between two bounding boxes in the form [x1,y1,x2,y2]
+    """
     xx1 = np.maximum(bb_det[0], bb_trk[0])
     xx2 = np.minimum(bb_det[2], bb_trk[2])
     w = np.maximum(0., xx2 - xx1)
@@ -72,7 +72,7 @@ def iou_ext_sep(bb_det, bb_trk, ext_w, ext_h):
     return o
 
 
-@jit
+# @jit
 def outside(trk, img_s):
     """
     Computes how many percent of trk is placed outside of img_s
@@ -92,7 +92,7 @@ def outside(trk, img_s):
     return out_a / area
 
 
-@jit
+# @jit
 def area_cost(bb_det, bb_trk):
     """
     This cost compute the difference between bounding box sizes
@@ -124,13 +124,13 @@ def area_cost(bb_det, bb_trk):
     return ratio - 1
 
 
-@jit
+# @jit
 # intersection over union with limit on area
 def iou_la(bb_det, bb_trk):
     """
-  Computes IOU (Intersection Over Union) between two bounding boxes in the form [x1,y1,x2,y2]
-  with limitation on percentage of change of area
-  """
+    Computes IOU (Intersection Over Union) between two bounding boxes in the form [x1,y1,x2,y2]
+    with limitation on percentage of change of area
+    """
     xx1 = np.maximum(bb_det[0], bb_trk[0])
     xx2 = np.minimum(bb_det[2], bb_trk[2])
     w = np.maximum(0., xx2 - xx1)
@@ -153,11 +153,11 @@ def iou_la(bb_det, bb_trk):
     return o
 
 
-@jit
+# @jit
 def ios(bb_first, bb_second):
     """
-  Computes IOS (Intersection Over Second Bounding Box) between two bounding boxes in the form [x1,y1,x2,y2]
-  """
+    Computes IOS (Intersection Over Second Bounding Box) between two bounding boxes in the form [x1,y1,x2,y2]
+    """
     xx1 = np.maximum(bb_first[0], bb_second[0])
     yy1 = np.maximum(bb_first[1], bb_second[1])
     xx2 = np.minimum(bb_first[2], bb_second[2])
@@ -208,5 +208,3 @@ def cal_ios_matrix(trackers):
             if t2 != t1:
                 ios_matrix[t1, t2] = ios(trk1, trk2)
     return ios_matrix
-
-
diff --git a/libs/convert.py b/libs/convert.py
index e97165e..124bced 100644
--- a/libs/convert.py
+++ b/libs/convert.py
@@ -3,10 +3,10 @@
 
 def bbox_to_z(bbox):
     """
-  Takes a bounding box in the form [x1,y1,x2,y2] and returns z in the form
+    Takes a bounding box in the form [x1,y1,x2,y2] and returns z in the form
     [x,y,s,r] where x,y is the centre of the box and s is the scale/area and r is
     the aspect ratio
-  """
+    """
     w = bbox[2] - bbox[0]
     h = bbox[3] - bbox[1]
     x = bbox[0] + w / 2.
@@ -18,9 +18,9 @@ def bbox_to_z(bbox):
 
 def x_to_bbox(x, score=None):
     """
-  Takes a bounding box in the centre form [x,y,s,r] and returns it in the form
+    Takes a bounding box in the centre form [x,y,s,r] and returns it in the form
     [x1,y1,x2,y2] where x1,y1 is the top left and x2,y2 is the bottom right
-  """
+    """
     w = np.sqrt(x[2] * x[3])
     h = x[2] / w
     if score is None:
diff --git a/libs/kalman_tracker.py b/libs/kalman_tracker.py
index f083629..51df1d4 100644
--- a/libs/kalman_tracker.py
+++ b/libs/kalman_tracker.py
@@ -5,39 +5,66 @@
 
 class KalmanBoxTracker(object):
     """
-  This class represents the internel state of individual tracked objects observed as bbox.
-  """
+    This class represents the internal state of individual tracked objects observed as bbox.
+    """
+
     count = 0
 
     def __init__(self, bbox, init_mode, bbox_before):
         """
-    Initialises a tracker using initial bounding box.
-    """
+        Initialises a tracker using initial bounding box.
+        """
         # define constant velocity model
         # (u, v, s, r, u_dot, v_dot, s_dot) -> (u,v): location center, s: area, r: aspect ratio
+        # dim_x: Number of state variables for the Kalman filter
+        # dim_z: Number of of measurement inputs a.k.a observations
         self.kf = KalmanFilter(dim_x=7, dim_z=4)
+        # state transition matrix
         self.kf.F = np.array(
-            [[1, 0, 0, 0, 1, 0, 0], [0, 1, 0, 0, 0, 1, 0], [0, 0, 1, 0, 0, 0, 1], [0, 0, 0, 1, 0, 0, 0],
-             [0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0, 1]])
+            [[1, 0, 0, 0, 1, 0, 0],
+             [0, 1, 0, 0, 0, 1, 0],
+             [0, 0, 1, 0, 0, 0, 1],
+             [0, 0, 0, 1, 0, 0, 0],
+             [0, 0, 0, 0, 1, 0, 0],
+             [0, 0, 0, 0, 0, 1, 0],
+             [0, 0, 0, 0, 0, 0, 1]])
+        # measurement function a.k.a stateProjection
         self.kf.H = np.array(
-            [[1, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0]])
+            [[1, 0, 0, 0, 0, 0, 0],
+             [0, 1, 0, 0, 0, 0, 0],
+             [0, 0, 1, 0, 0, 0, 0],
+             [0, 0, 0, 1, 0, 0, 0]])
 
         if init_mode == 0:
+            # Measurement noise matrix
             self.kf.R[2:, 2:] *= 1.   # 10.
+
+            # covariance matrix
             self.kf.P[4:, 4:] *= 10.  # 1000. # give high uncertainty to the unobservable initial velocities
             self.kf.P *= 10.
+
+            # Process uncertainty/noise
             self.kf.Q[-1, -1] *= 0.01
             self.kf.Q[4:, 4:] *= 0.01
+
+            # filter state estimate
             self.kf.x[:4] = convert.bbox_to_z(bbox)
 
         elif init_mode == 1:
+            # Measurement noise matrix
             self.kf.R[2:, 2:] *= 1.
+
+            # covariance matrix
             self.kf.P[4:, 4:] *= 10.  # give high uncertainty to the unobservable initial velocities
             # self.kf.P *= 10.
+
+            # Process uncertainty/noise
             self.kf.Q[-1, -1] *= 0.01
             self.kf.Q[4:, 4:] *= 0.01
+
             state_before = convert.bbox_to_z(bbox_before)
             state = convert.bbox_to_z(bbox)
+            # filter state estimate
             self.kf.x[:4] = state
             self.kf.x[4:] = state[0:3] - state_before[0:3]
 
@@ -51,8 +78,8 @@ def __init__(self, bbox, init_mode, bbox_before):
 
     def update(self, bbox, isz):
         """
-    Updates the state vector with observed bbox.
-    """
+        Updates the state vector with observed bbox.
+        """
         self.time_since_update = 0
         if isz == 0:
             # decrease area change ratio
@@ -64,18 +91,33 @@ def update(self, bbox, isz):
 
     def predict(self):
         """
-    Advances the state vector and returns the predicted bounding box estimate.
-    """
+        Advances the state vector and returns the predicted bounding box estimate.
+        """
         # to prevent area become negative after prediction, make zero the rate of area change
         if (self.kf.x[6] + self.kf.x[2]) <= 0:
             self.kf.x[6] *= 0.0
         self.kf.predict()
         self.age += 1
         self.time_since_update += 1
+
         return convert.x_to_bbox(self.kf.x)
 
     def get_state(self):
         """
-    Returns the current bounding box estimate.
-    """
+        Returns the current bounding box estimate.
+        """
         return convert.x_to_bbox(self.kf.x)
+
+    def to_json(self):
+        return {
+            "time_since_observed": self.time_since_observed,
+            "confidence": self.confidence if isinstance(self.confidence, float) or isinstance(self.confidence, int) else float(self.confidence.tolist()[0]),
+            "age": self.age,
+            "get_state": self.get_state().tolist(),
+            "time_since_update": self.time_since_update,
+            "id": self.id,
+            "kf": {
+                "x": self.kf.x.tolist(),
+                "covariance": self.kf.P.tolist()
+            }
+        }
diff --git a/libs/tracker.py b/libs/tracker.py
index 6d30404..c1ae341 100644
--- a/libs/tracker.py
+++ b/libs/tracker.py
@@ -1,15 +1,118 @@
 import numpy as np
+
+from . import utils
 from . import association
 from . import kalman_tracker
 from . import calculate_cost
 
 
+def _init_area_and_trackers(kalman_trackers):
+    """
+    WARNING: This writes to kalman_trackers
+    """
+    trks = np.zeros((len(kalman_trackers), 5))
+    to_del = []
+    area_sum = 0
+    for t, trk in enumerate(trks):
+        pos = kalman_trackers[t].predict()[0]
+        trk[:] = [pos[0], pos[1], pos[2], pos[3], 0]
+        area_sum = area_sum + kalman_trackers[t].kf.x[2]
+
+        if not np.isscalar(area_sum):
+            area_sum = area_sum[0]
+        if np.any(np.isnan(pos)):
+            to_del.append(t)
+    area_avg = 0
+    if len(kalman_trackers) > 0:
+        area_avg = area_sum / len(kalman_trackers)
+    trks = np.ma.compress_rows(np.ma.masked_invalid(trks))
+    for t in reversed(to_del):
+        kalman_trackers.pop(t)
+
+    return trks, area_avg
+
+
+def _remove_outside_trackers(trks, kalman_trackers, scene):
+    """
+    Warning: Writes to trks, kalman_trackers
+    """
+    outside = calculate_cost.cal_outside(trks, scene)
+    to_del = []
+    for t in range(len(outside)):
+        if outside[t] > 0.5:
+            to_del.append(t)
+    for t in reversed(to_del):
+        kalman_trackers.pop(t)
+        trks = np.delete(trks, t, 0)
+
+    return trks
+
+
+def _update_matched_trackers(dets, kalman_trackers, unmatched_trks, occluded_trks, matched, trks):
+    """
+    update matched trackers with assigned detections
+    """
+    unmatched_trks_pos = []
+    if len(dets) > 0:
+        for t, trk in enumerate(kalman_trackers):
+            if t not in unmatched_trks:
+                if t not in occluded_trks:
+                    # Update according to associated detection
+                    d = matched[np.where(matched[:, 1] == t)[0], 0]
+                    trk.update(dets[d, :][0], 1)
+                else:
+                    # Update according to estimated bounding box
+                    trk.update(trks[t, :], 0)
+            else:
+                unmatched_trks_pos.append(np.concatenate((trk.get_state()[0], [trk.id + 1])).reshape(1, -1))
+
+    return unmatched_trks_pos
+
+
+def _build_new_targets(unmatched_before_before, unmatched_before, unmatched, area_avg, kalman_trackers):
+    if (len(unmatched_before_before) != 0) and (len(unmatched_before) != 0) and (len(unmatched) != 0):
+        new_trackers, del_ind = association.find_new_trackers_2(unmatched, unmatched_before,
+                                                                unmatched_before_before, area_avg)
+        if len(new_trackers) > 0:
+            unm = np.asarray(unmatched)
+            unmb = np.asarray(unmatched_before)
+            unmbb = np.asarray(unmatched_before_before)
+            # sort del_ind in descending order so removing step by step do not make error
+            del_ind = np.sort(del_ind, axis=0)
+            del_ind = np.flip(del_ind, axis=0)
+            for i, new_tracker in enumerate(new_trackers):
+                new_trk_certainty = unm[new_tracker[0], 4] + unmb[new_tracker[1], 4] + unmbb[new_tracker[2], 4]
+                if new_trk_certainty > 2:
+                    trk = kalman_tracker.KalmanBoxTracker(unm[new_tracker[0], :], 1, unmb[new_tracker[1], :])
+                    kalman_trackers.append(trk)
+                    # remove matched detection from unmatched arrays
+                    unmatched.pop(del_ind[i, 0])
+                    unmatched_before.pop(del_ind[i, 1])
+                    unmatched_before_before.pop(del_ind[i, 2])
+
+
+def _remove_dead_tracklet(kalman_trackers, max_age):
+    ret = []
+    i = len(kalman_trackers)
+    for trk in reversed(kalman_trackers):
+        d = trk.get_state()[0]
+        if trk.time_since_update < 1:
+            # +1 as MOT benchmark requires positive
+            ret.append(np.concatenate((d, [trk.id + 1])).reshape(1, -1))
+        i -= 1
+        # remove dead tracklet
+        if trk.time_since_update > (np.minimum(7, max_age + kalman_trackers[i].age / 10)):
+            kalman_trackers.pop(i)
+
+    return ret
+
+
 class Sort_OH(object):
 
-    def __init__(self, max_age=3, min_hits=3):
+    def __init__(self, max_age=3, min_hits=3, scene=np.array([1920, 1080])):
+        """
+        Sets key parameters for SORT
         """
-    Sets key parameters for SORT
-    """
         self.max_age = max_age
         self.min_hits = min_hits
         self.trackers = []
@@ -18,77 +121,56 @@ def __init__(self, max_age=3, min_hits=3):
         self.unmatched_before_before = []
         self.unmatched_before = []
         self.unmatched = []
-        self.seq = []
+        self.scene = scene
         self.conf_trgt = 0
         self.conf_objt = 0
 
+    def to_json(self):
+        """
+        Returns a dict object that can be used to serialize this a JSON
+        """
+        return {
+            "area_avg_array": list(map(lambda x: x if np.isscalar(x) else x[0], self.area_avg_array)),
+            "conf_objt": self.conf_objt,
+            "conf_trgt": self.conf_trgt,
+            "frame_count": self.frame_count,
+            "max_age": self.max_age,
+            "min_hits": self.min_hits,
+            "scene": self.scene.tolist(),
+            "trackers": list(map(lambda x: x.to_json(), self.trackers)),
+            "unmatched": list(map(lambda x: x.tolist(), self.unmatched)),
+            "unmatched_before": list(map(lambda x: x.tolist(), self.unmatched_before)),
+            "unmatched_before_before": list(map(lambda x: x.tolist(), self.unmatched_before_before)),
+        }
+
     def update(self, dets, gts):
         """
-    Params:
-      dets - a numpy array of detections in the format [[x1,y1,x2,y2,score],[x1,y1,x2,y2,score],...]
-    Requires: this method must be called once for each frame even with empty detections.
-    Returns the a similar array, where the last column is the object ID.
+        Params:
+          dets - a numpy array of detections in the format [[x1,y1,x2,y2,score],[x1,y1,x2,y2,score],...]
+        Requires: this method must be called once for each frame even with empty detections.
+        Returns the a similar array, where the last column is the object ID.
 
-    NOTE: The number of objects returned may differ from the number of detections provided.
-    """
+        NOTE: The number of objects returned may differ from the number of detections provided.
+        """
         self.frame_count += 1
-        # get predicted locations from existing trackers.
-        trks = np.zeros((len(self.trackers), 5))
-        to_del = []
-        ret = []
-        area_sum = 0
-        for t, trk in enumerate(trks):
-            pos = self.trackers[t].predict()[0]
-            trk[:] = [pos[0], pos[1], pos[2], pos[3], 0]
-            area_sum = area_sum + self.trackers[t].kf.x[2]
-            if np.any(np.isnan(pos)):
-                to_del.append(t)
-        area_avg = 0
-        if len(self.trackers) > 0:
-            area_avg = area_sum/len(self.trackers)
+
+        trks, area_avg = _init_area_and_trackers(self.trackers)
         self.area_avg_array.append(area_avg)
-        trks = np.ma.compress_rows(np.ma.masked_invalid(trks))
-        for t in reversed(to_del):
-            self.trackers.pop(t)
-        # remove outside image trackers
-        to_del = []
-        if self.seq == 'MOT17-05-DPM' or self.seq == 'MOT17-05-FRCNN' or self.seq == 'MOT17-05-SDP' \
-           or self.seq == 'MOT17-05-POI' or self.seq == 'MOT17-06-DPM' or self.seq == 'MOT17-06-FRCNN' \
-           or self.seq == 'MOT17-06-SDP' or self.seq == 'MOT17-06-POI':
-            scene = np.array([640, 480])
-        else:
-            scene = np.array([1920, 1080])
-        outside = calculate_cost.cal_outside(trks, scene)
-        for t in range(len(outside)):
-            if outside[t] > 0.5:
-                to_del.append(t)
-        for t in reversed(to_del):
-            self.trackers.pop(t)
-            trks = np.delete(trks, t, 0)
+
+        trks = _remove_outside_trackers(trks, self.trackers, self.scene)
 
         matched, unmatched_dets, unmatched_trks, occluded_trks, unmatched_gts = association.associate_detections_to_trackers(self, dets, trks, gts, area_avg)
 
         # update matched trackers with assigned detections
-        unmatched_trks_pos = []
-        if len(dets) > 0:
-            for t, trk in enumerate(self.trackers):
-                if t not in unmatched_trks:
-                    if t not in occluded_trks:
-                        # Update according to associated detection
-                        d = matched[np.where(matched[:, 1] == t)[0], 0]
-                        trk.update(dets[d, :][0], 1)
-                    else:
-                        # Update according to estimated bounding box
-                        trk.update(trks[t, :], 0)
-                else:
-                    unmatched_trks_pos.append(np.concatenate((trk.get_state()[0], [trk.id + 1])).reshape(1, -1))
+        unmatched_trks_pos = _update_matched_trackers(dets, self.trackers, unmatched_trks, occluded_trks, matched, trks)
 
         # create and initialise new trackers for unmatched detections
         if self.frame_count <= self.min_hits:
             for i in unmatched_dets:
                 # Put condition on uncertainty
                 if dets[i, 4] > 0.6:
-                    trk = kalman_tracker.KalmanBoxTracker(dets[i, :], 0, dets[i, :]) # put dets[i, :] as dummy data for last argument
+                    # put dets[i, :] as dummy data for last argument
+                    trk = kalman_tracker.KalmanBoxTracker(dets[i, :], 0, dets[i, :])
                     self.trackers.append(trk)
         else:
             self.unmatched = []
@@ -96,24 +178,8 @@ def update(self, dets, gts):
                 self.unmatched.append(dets[i, :])
 
             # Build new targets
-            if (len(self.unmatched_before_before) != 0) and (len(self.unmatched_before) != 0) and (len(self.unmatched) != 0):
-                new_trackers, del_ind = association.find_new_trackers_2(self.unmatched, self.unmatched_before, self.unmatched_before_before, area_avg)
-                if len(new_trackers) > 0:
-                    unm = np.asarray(self.unmatched)
-                    unmb = np.asarray(self.unmatched_before)
-                    unmbb = np.asarray(self.unmatched_before_before)
-                    # sort del_ind in descending order so removing step by step do not make error
-                    del_ind = np.sort(del_ind, axis=0)
-                    del_ind = np.flip(del_ind, axis=0)
-                    for i, new_tracker in enumerate(new_trackers):
-                        new_trk_certainty = unm[new_tracker[0], 4] + unmb[new_tracker[1], 4] + unmbb[new_tracker[2], 4]
-                        if new_trk_certainty > 2:
-                            trk = kalman_tracker.KalmanBoxTracker(unm[new_tracker[0], :], 1, unmb[new_tracker[1], :])
-                            self.trackers.append(trk)
-                            # remove matched detection from unmatched arrays
-                            self.unmatched.pop(del_ind[i, 0])
-                            self.unmatched_before.pop(del_ind[i, 1])
-                            self.unmatched_before_before.pop(del_ind[i, 2])
+            _build_new_targets(self.unmatched_before_before, self.unmatched_before, self.unmatched,
+                               self.area_avg_array[len(self.area_avg_array) - 1], self.trackers)
             self.unmatched_before_before = self.unmatched_before
             self.unmatched_before = self.unmatched
 
@@ -122,15 +188,8 @@ def update(self, dets, gts):
         for g in unmatched_gts:
             unmatched_gts_pos.append(gts[g, :].reshape(1, 5))
 
-        i = len(self.trackers)
-        for trk in reversed(self.trackers):
-            d = trk.get_state()[0]
-            if trk.time_since_update < 1:
-                ret.append(np.concatenate((d, [trk.id + 1])).reshape(1, -1))  # +1 as MOT benchmark requires positive
-            i -= 1
-            # remove dead tracklet
-            if trk.time_since_update > (np.minimum(7, self.max_age + self.trackers[i].age/10)):
-                self.trackers.pop(i)
+        ret = _remove_dead_tracklet(self.trackers, self.max_age)
+
         out1 = np.empty((0, 5))
         out2 = np.empty((0, 5))
         out3 = np.empty((0, 5))
@@ -141,4 +200,5 @@ def update(self, dets, gts):
             out2 = np.concatenate(unmatched_trks_pos)
         if len(unmatched_gts_pos) > 0:
             out3 = np.concatenate(unmatched_gts_pos)
+
         return out1, out2, out3
diff --git a/libs/utils.py b/libs/utils.py
new file mode 100644
index 0000000..8768148
--- /dev/null
+++ b/libs/utils.py
@@ -0,0 +1,53 @@
+import numpy as np
+
+
+class DictObj:
+    def __init__(self, in_dict: dict):
+        assert isinstance(in_dict, dict)
+
+        self.in_dict = in_dict
+
+        for key, val in in_dict.items():
+            if isinstance(val, (list, tuple)):
+                setattr(self, key, [DictObj(x) if isinstance(x, dict) else x for x in val])
+            else:
+                setattr(self, key, DictObj(val) if isinstance(val, dict) else val)
+
+    def __eq__(self, o: object) -> bool:
+        if not isinstance(o, DictObj):
+            return False
+
+        for key, value in vars(self).items():
+            if callable(value):
+                continue
+            if o.__getattribute__(key) != value:
+                return False
+        return True
+
+    def to_json(self):
+        return {}
+
+
+def list_of_ndarray_tolist(l):
+    """
+    Convert a list of ndarray's to a plain Python list
+
+    This is useful if you want to dump the list via JSON or similar.
+
+    @param l The list to convert
+    @return l as a list of same dimensions but without any ndarray in it
+    """
+    if isinstance(l, list) and (
+            len(l) > 0 and (isinstance(l[0], list) or isinstance(l[0], float) or isinstance(l[0], int))):
+        ret_unsafe = l
+    else:
+        # Convert to a list first, this list may contain int64 and is therefore not safe for json
+        ret_unsafe = list(map(lambda x: x.tolist(), l)) if len(l) > 0 else []
+
+    ret = []
+    for x in ret_unsafe:
+        if type(x) == type(np.int64):
+            ret.append(int(x))
+        else:
+            ret.append(x)
+    return ret
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..749cf3d
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,7 @@
+tqdm
+opencv-python
+filterpy
+numpy
+scipy
+numba
+matplotlib
diff --git a/spec/__init__.py b/spec/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/spec/libs/__init__.py b/spec/libs/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/spec/libs/test_association_associate_detections_to_trackers.py b/spec/libs/test_association_associate_detections_to_trackers.py
new file mode 100644
index 0000000..f210998
--- /dev/null
+++ b/spec/libs/test_association_associate_detections_to_trackers.py
@@ -0,0 +1,332 @@
+import json
+import unittest
+
+import numpy as np
+
+from libs import association
+
+
+class DictObj:
+    def __init__(self, in_dict: dict):
+        assert isinstance(in_dict, dict)
+
+        self.in_dict = in_dict
+
+        for key, val in in_dict.items():
+            if isinstance(val, (list, tuple)):
+                setattr(self, key, [DictObj(x) if isinstance(x, dict) else x for x in val])
+            else:
+                setattr(self, key, DictObj(val) if isinstance(val, dict) else val)
+
+    def __eq__(self, o: object) -> bool:
+        if not isinstance(o, DictObj):
+            return False
+
+        for key, value in vars(self).items():
+            if o.__getattribute__(key) != value:
+                return False
+        return True
+
+
+class TestAssociation(unittest.TestCase):
+    def test_empty_detections_and_trackers(self):
+        dets = []
+        matched, unmatched_dets, unmatched_trks, occluded_trks, unmatched_gts = association.associate_detections_to_trackers(
+            None, dets, list(), None, None, iou_threshold=0.3)
+        self.assertListEqual(list(), matched.tolist())
+        self.assertListEqual(list(), unmatched_dets.tolist())
+        self.assertListEqual(list(), unmatched_trks.tolist())
+        self.assertListEqual(list(), occluded_trks.tolist())
+        self.assertListEqual(list(), unmatched_gts.tolist())
+
+    def test_detections_and_empty_trackers(self):
+        dets = [[0, 1, 2, 4], [2, 3, 4, 5]]
+        matched, unmatched_dets, unmatched_trks, occluded_trks, unmatched_gts = association.associate_detections_to_trackers(
+            None, dets, list(), None, None, iou_threshold=0.3)
+        self.assertListEqual(list(), matched.tolist())
+        self.assertListEqual([0, 1], unmatched_dets.tolist())
+        self.assertListEqual(list(), unmatched_trks.tolist())
+        self.assertListEqual(list(), occluded_trks.tolist())
+        self.assertListEqual(list(), unmatched_gts.tolist())
+
+    def test_empty_detections_trackers_full(self):
+        dets = []
+        tracker = [[1, 2, 3, 4]]
+        matched, unmatched_dets, unmatched_trks, occluded_trks, unmatched_gts = association.associate_detections_to_trackers(
+            None, dets, tracker, None, None, iou_threshold=0.3)
+        self.assertListEqual(list(), matched.tolist())
+        self.assertListEqual([], unmatched_dets.tolist())
+        self.assertListEqual(list(), unmatched_trks.tolist())
+        self.assertListEqual(list(), occluded_trks.tolist())
+        self.assertListEqual(list(), unmatched_gts.tolist())
+
+    def assert_associate_detections_to_trackers(self, x):
+        matched, unmatched_dets, unmatched_trks, occluded_trks, unmatched_gts = association.associate_detections_to_trackers(
+            DictObj(x['mot_tracker']), np.array(x['detections']), np.array(x['trackers']), x['groundtruths'],
+            x['average_area'], x['iou_threshold'])
+
+        self.assertListEqual(x['ret_matches'], matched.tolist())
+        self.assertListEqual(x['ret_unmatched_detections'], unmatched_dets.tolist())
+        self.assertListEqual(x['ret_unmatched_trackers'],
+                             unmatched_trks if isinstance(unmatched_trks, list) else unmatched_trks.tolist())
+        self.assertListEqual(x['ret_occluded_trackers'], occluded_trks.tolist())
+        self.assertListEqual(x['ret_unmatched_groundtruths'], unmatched_gts.tolist())
+
+    def test_associate_detections_to_trackers_pass_1(self):
+        self.assert_associate_detections_to_trackers({"mot_tracker":{"trackers":[{"time_since_observed":17,"confidence":1,"age":368},{"time_since_observed":14,"confidence":1,"age":273},{"time_since_observed":0,"confidence":1,"age":219},{"time_since_observed":1,"confidence":1,"age":200},{"time_since_observed":0,"confidence":0.07305242074847638,"age":89},{"time_since_observed":0,"confidence":0.5,"age":73},{"time_since_observed":0,"confidence":0.5,"age":72},{"time_since_observed":0,"confidence":0.5,"age":59},{"time_since_observed":0,"confidence":0.2658236378313566,"age":45}],"frame_count":588,"min_hits":3,"conf_trgt":0.35,"conf_objt":0.75,"unmatched_before":[[549.75,429.71,591.621,557.3199999999999,0.42543]]},"detections":[[721.23,438.28,763.101,565.89,1.8465],[585.82,449.36,617.3100000000001,545.83,1.5789],[1085.4,423.72,1176.296,698.4100000000001,1.5448],[1232.4,442.1,1323.296,716.79,1.261],[625,449,664,568,1.151],[555.37,459.21,582.654,543.063,0.51894],[549.75,429.71,591.621,557.3199999999999,0.32455]],"trackers":[[1763.14973972401,375.60857521554806,2017.9606713207927,1142.0506392053612,0],[1164.1812513483892,420.29061997713836,1249.2239974592244,677.4202811074523,0],[1234.1549899593497,439.2151767991311,1327.5242642314074,721.3242702278906,0],[1017.4534659341475,433.66146589620973,1090.8894501218451,655.9696782352675,0],[585.8126568669982,449.3533147315173,617.3895569059339,546.0907701507795,0],[1089.0090407048692,404.7725134807381,1180.014053198618,679.7837427323002,0],[624.6923113598453,449.67069132486915,663.1892759653186,567.1633865221663,0],[725.626516162457,441.5873402727734,761.8188380744704,552.1721161757416,0],[560.6978829220411,460.27153351692573,586.1698374058055,538.6879691125774,0]],"groundtruths":[[912,484,1009,593,0],[1585,-1,1921,577,0],[1163,441,1196,530,0],[1308,431,1342,549,0],[907,414,937,553,0],[1118,429,1160,574,1],[1062,445,1103,569,1],[996,443,1034,547,1],[989,448,1028,549,1],[900,448,936,549,1],[835,473,887,548,0],[796,476,851,536,0],[382,463,428,576,1],[428,458,457,562,1],[424,457,453,565,1],[417,469,457,569,1],[371,447,413,551,0],[427,457,461,540,1],[1090,414,1195,697,1],[1180,432,1254,677,1],[1236,442,1320,714,1],[992,433,1081,672,1],[630,453,662,557,1],[665,454,702,558,1],[722,448,765,551,1],[668,462,693,534,0],[694,462,713,533,0],[711,479,728,536,0],[587,457,620,543,1],[557,461,584,543,1],[910,408,936,537,0],[545,460,570,534,1],[587,440,606,484,1],[587,447,605,490,1],[565,441,577,471,1],[576,435,589,467,1],[985,458,1009,512,0],[1012,454,1030,515,0],[784,452,811,506,1],[758,481,821,510,0]],"average_area":[33159.49929365327],"iou_threshold":0.3,"ret_matches":[[0,7],[1,4],[2,5],[3,2],[4,6],[5,8]],"ret_unmatched_detections":[6],"ret_unmatched_trackers":[],"ret_occluded_trackers":[0,1,3],"ret_unmatched_groundtruths":[]})
+
+    def test_associate_detections_to_trackers_pass_2(self):
+        self.assert_associate_detections_to_trackers({ 'mot_tracker':{ 'trackers':[{ 'time_since_observed':0, 'confidence':0.5, 'age':32 }, { 'time_since_observed':0, 'confidence':0.5, 'age':32 }, { 'time_since_observed':5, 'confidence':0.8034821074946065, 'age':27 }, { 'time_since_observed':0, 'confidence':0.5, 'age':17 }, { 'time_since_observed':0, 'confidence':0.5, 'age':12 }], 'frame_count':33, 'min_hits':3, 'conf_trgt':0.35, 'conf_objt':0.75, 'unmatched_before':[[1450, 429.71, 1491.871, 557.3199999999999, 0.9037]] }, 'detections':[[1613.3, 389.14, 1761.59, 836, 2.5745], [607.51, 442.1, 698.406, 716.79, 1.5007], [1450, 429.71, 1491.871, 557.3199999999999, 1.0982], [497.24, 442.1, 588.136, 716.79, 1.0672], [1254.6, 446.72, 1288.422, 550.19, 0.83953], [672.78, 433.93, 746.423, 656.86, 0.43969]], 'trackers':[[1626.9999442228197, 388.1208918913693, 1776.6864681805498, 839.1892449129183, 0], [586.6684833277264, 387.4658788667176, 699.2957247119476, 727.3942309096743, 0], [1721.75360678811, 390.4019263280605, 1860.0098235582702, 807.1669547031725, 0], [1249.5734942682582, 443.2621949826761, 1284.8884336580843, 551.2218934174055, 0], [498.4135397852741, 445.4433065035752, 591.869205281981, 727.8117020759506, 0]], 'groundtruths':[[912, 484, 1009, 593, 0], [1607, 396, 1799, 842, 1], [602, 443, 690, 714, 1], [1585, -1, 1921, 577, 0], [1163, 441, 1196, 530, 0], [1308, 431, 1342, 549, 0], [907, 414, 937, 553, 0], [1683, 416, 1908, 809, 1], [1055, 483, 1091, 593, 1], [1090, 484, 1122, 598, 1], [718, 491, 763, 573, 0], [679, 492, 731, 597, 0], [731, 461, 759, 535, 0], [1258, 447, 1291, 547, 1], [1012, 441, 1052, 557, 1], [1099, 440, 1137, 548, 1], [929, 435, 972, 549, 1], [474, 444, 588, 736, 1], [655, 461, 724, 657, 1], [1448, 430, 1508, 560, 1], [1555, 436, 1607, 558, 1], [500, 459, 588, 710, 1], [835, 473, 887, 548, 0], [796, 476, 851, 536, 0], [547, 463, 582, 556, 1], [496, 456, 523, 558, 1], [375, 446, 416, 550, 0], [419, 459, 458, 543, 1], [581, 455, 617, 590, 1], [1005, 454, 1037, 531, 1], [640, 456, 679, 587, 1], [698, 462, 725, 529, 0], [712, 477, 731, 534, 0], [733, 503, 763, 554, 0], [910, 408, 936, 537, 0], [710, 516, 749, 582, 0], [679, 528, 725, 607, 0], [985, 459, 1004, 512, 0], [1003, 453, 1021, 514, 0], [578, 427, 598, 470, 1], [595, 424, 613, 466, 1], [1027, 449, 1051, 518, 1], [700, 449, 733, 539, 1]], 'average_area':[38725.18380509759], 'iou_threshold':0.3, 'ret_matches':[[0, 0], [1, 1], [3, 4], [4, 3]], 'ret_unmatched_detections':[5, 2], 'ret_unmatched_trackers':[], 'ret_occluded_trackers':[2], 'ret_unmatched_groundtruths':[] })
+
+    def test_associate_detections_to_trackers_pass_3(self):
+        self.assert_associate_detections_to_trackers({"mot_tracker":{"trackers":[{"time_since_observed":2,"confidence":1,"age":47},{"time_since_observed":0,"confidence":0.5,"age":47},{"time_since_observed":0,"confidence":0.15166341194080873,"age":32},{"time_since_observed":0,"confidence":0.5,"age":27},{"time_since_observed":2,"confidence":0.11961138221960334,"age":15}],"frame_count":48,"min_hits":3,"conf_trgt":0.35,"conf_objt":0.75,"unmatched_before":[]},"detections":[[618.34,446.86,703.082,703.09,1.2516],[486.58,444.35,591.14,760.03,0.97536],[579.94,451.29,624.888,588.13,0.31026]],"trackers":[[1786.8317504623678,382.8226477084003,1956.5894503077338,894.1241467639411,0],[617.8662874567835,443.9569042506989,705.9295617350076,710.1546678694592,0],[1255.03806910781,442.17287175903624,1290.9762334980953,551.9952275625799,0],[486.4927226793391,444.3220291921217,591.6950224027397,761.9413603902946,0],[1482.9591357034267,430.0855507067737,1524.6639800075843,557.1891628663964,0]],"groundtruths":[[912,484,1009,593,0],[1743,389,1958,884,1],[621,442,709,718,1],[1585,-1,1921,577,0],[1163,441,1196,530,0],[1308,431,1342,549,0],[907,414,937,553,0],[1054,483,1091,593,1],[1090,484,1122,598,1],[709,486,753,577,0],[679,492,731,597,0],[738,461,772,539,0],[1260,447,1293,547,1],[1008,441,1048,557,1],[1098,440,1136,548,1],[928,435,971,549,1],[480,439,593,756,1],[669,459,742,664,1],[1482,430,1539,559,1],[500,454,588,720,1],[835,473,887,548,0],[796,476,851,536,0],[547,462,582,556,1],[496,456,524,558,1],[374,446,416,550,0],[419,459,458,543,1],[582,455,622,589,1],[1019,455,1051,532,1],[646,456,683,579,1],[697,462,725,529,0],[712,477,731,534,0],[738,500,774,558,0],[910,408,936,537,0],[705,518,744,584,0],[679,528,725,607,0],[610,462,636,536,1],[985,459,1004,512,0],[1003,453,1021,514,0],[578,424,598,467,1],[595,423,613,465,1],[1027,450,1051,519,1],[717,447,750,542,1]],"average_area":[30580.30947756609],"iou_threshold":0.3,"ret_matches":[[0,1],[1,3]],"ret_unmatched_detections":[2],"ret_unmatched_trackers":[4,2],"ret_occluded_trackers":[0],"ret_unmatched_groundtruths":[]})
+
+    def test_from_json_array(self):
+        with open('spec/res/associate_detections_to_trackers.json') as f:
+            invocations = json.load(f)
+
+            i = 0
+            for x in invocations:
+
+                if i == 30:
+                    # print(i)
+                    pass
+
+                self.assert_associate_detections_to_trackers(x)
+
+                i += 1
+
+    def test__unmatched_detections(self):
+        def test_and_expect(t):
+            actual = association._unmatched_detections(
+                np.array(t['detections']),
+                np.array(t['matched_indices'])
+            )
+            self.assertListEqual(t['ret_unmatched_detections'], actual)
+
+        test_and_expect(
+            {"detections": [[267.77, 437.53, 388.03, 800.3, 1.429], [961.31, 412.56, 1131.79, 926.01, 1.3889],
+                            [359.91, 423.24, 464.47, 738.9200000000001, 1.1941],
+                            [1087.1, 363.04, 1312.37, 1040.8600000000001, 1.0567],
+                            [566.69, 408.29, 678.83, 746.7, 0.88242],
+                            [936.71, 260.92, 1195.63, 1039.68, 0.38177], [60.714, 359.28, 209.004, 806.14, 0.33426]],
+             "matched_indices": [[0, 5], [1, 2], [2, 4], [3, 1], [4, 3], [5, 0]], "ret_unmatched_detections": [6]})
+        test_and_expect(
+            {"detections": [[267.77, 437.53, 388.03, 800.3, 1.8658], [961.31, 412.56, 1131.79, 926.01, 1.385],
+                            [1104.1, 394.97, 1300.08, 984.9200000000001, 1.1512],
+                            [566.69, 408.29, 678.83, 746.7, 1.1382],
+                            [56.715, 391.01, 195.005, 807.87, 0.79262], [359.91, 444.35, 464.47, 760.03, 0.7547],
+                            [988.7, 260.92, 1247.6200000000001, 1039.68, 0.38785],
+                            [725.08, 442.23, 780.649, 610.94, 0.31484]],
+             "matched_indices": [[0, 5], [1, 2], [2, 1], [3, 3], [5, 4], [6, 0]], "ret_unmatched_detections": [4, 7]})
+        test_and_expect({"detections": [[272.53, 430.92, 384.66999999999996, 769.33, 1.5459],
+                                        [680.04, 389.02, 800.3, 751.79, 1.1476],
+                                        [1041.9, 363.04, 1267.17, 1040.8600000000001, 0.99078],
+                                        [570.75, 442.1, 661.646, 716.79, 0.93738],
+                                        [872.16, 442.23, 927.7289999999999, 610.94, 0.8402],
+                                        [444.35, 444.35, 548.9100000000001, 760.03, 0.39837]],
+                         "matched_indices": [[0, 6], [1, 3], [2, 1], [3, 4], [4, 0], [5, 5]],
+                         "ret_unmatched_detections": []})
+        test_and_expect({"detections": [[272.53, 430.92, 384.66999999999996, 769.33, 1.7644],
+                                        [679.82, 408.29, 791.96, 746.7, 1.318],
+                                        [570.75, 442.1, 661.646, 716.79, 1.2338],
+                                        [1056.6, 381.02, 1266.7199999999998, 1013.38, 1.2235],
+                                        [872.16, 442.23, 927.7289999999999, 610.94, 1.0282],
+                                        [961.31, 412.56, 1131.79, 926.01, 0.66346],
+                                        [434.36, 454.06, 531.852, 748.53, 0.62246],
+                                        [936.71, 260.92, 1195.63, 1039.68, 0.4031]],
+                         "matched_indices": [[0, 6], [1, 3], [2, 4], [3, 1], [5, 2], [6, 5], [7, 0]],
+                         "ret_unmatched_detections": [4]})
+        test_and_expect(
+            {"detections": [[292.02, 437.53, 412.28, 800.3, 1.9404], [961.31, 412.56, 1131.79, 926.01, 1.7096],
+                            [1087.1, 363.04, 1312.37, 1040.8600000000001, 1.1698],
+                            [78.976, 416.87, 207.936, 805.75, 1.1452], [566.69, 408.29, 678.83, 746.7, 0.70813],
+                            [729.81, 472.58, 771.6809999999999, 600.1899999999999, 0.47918],
+                            [386.96, 442.1, 477.856, 716.79, 0.46466], [971.06, 292.02, 1212.57, 1018.56, 0.44398]],
+             "matched_indices": [[0, 5], [1, 2], [2, 1], [3, 6], [4, 3], [6, 4], [7, 0]],
+             "ret_unmatched_detections": [5]})
+        test_and_expect(
+            {"detections": [[292.02, 437.53, 412.28, 800.3, 2.0159], [961.31, 412.56, 1131.79, 926.01, 1.633],
+                            [78.976, 416.87, 207.936, 805.75, 1.592],
+                            [1087.1, 363.04, 1312.37, 1040.8600000000001, 1.2742],
+                            [589.31, 408.29, 701.4499999999999, 746.7, 0.96689],
+                            [971.06, 292.02, 1212.57, 1018.56, 0.71655],
+                            [729.81, 472.58, 771.6809999999999, 600.1899999999999, 0.63224]],
+             "matched_indices": [[0, 5], [1, 2], [2, 6], [3, 1], [4, 3], [5, 0], [6, 4]],
+             "ret_unmatched_detections": []})
+        test_and_expect({"detections": [[317.68, 444.35, 422.24, 760.03, 1.6334],
+                                        [544.06, 408.29, 656.1999999999999, 746.7, 1.5398],
+                                        [946.52, 394.97, 1142.5, 984.9200000000001, 1.2071],
+                                        [1098.8, 381.02, 1308.92, 1013.38, 1.1404],
+                                        [988.7, 260.92, 1247.6200000000001, 1039.68, 0.40145]],
+                         "matched_indices": [[0, 4], [1, 3], [2, 2], [3, 1], [4, 0]], "ret_unmatched_detections": []})
+
+    def test__unmatched_trackers(self):
+        def test_and_expect(t):
+            actual = association._unmatched_trackers(
+                np.array(t['trackers']),
+                np.array(t['matched_indices'])
+            )
+            self.assertListEqual(t['ret_unmatched_trackers'], actual)
+
+        test_and_expect(
+            {"trackers": [[1790.6476379489334, 373.91066521274126, 1968.5101672757476, 909.523056077836, 0.0],
+                          [1505.4633627177443, 391.90041890267406, 1747.0613688975798, 1118.7053699971532, 0.0],
+                          [1347.7481219417173, 422.33142659797426, 1558.2940650143782, 1055.9737792924616, 0.0],
+                          [1105.1864132273108, 423.09464145899426, 1163.7721975747336, 600.8552098264788, 0.0],
+                          [932.0860439617203, 439.3534967610465, 1025.287878045643, 720.958009856063, 0.0],
+                          [721.7384330895677, 423.8494779654868, 812.5282482908349, 698.2185814162792, 0.0],
+                          [1351.5896356396593, 343.87800359776406, 1629.550948111278, 1179.7638198412108, 0.0],
+                          [572.7173986943344, 452.95222766192836, 600.7062398777271, 538.9719657939457, 0.0]],
+             "matched_indices": [[0, 2], [1, 0], [2, 5], [3, 1], [4, 3], [5, 4], [6, 6]],
+             "ret_unmatched_trackers": [7]})
+        test_and_expect(
+            {"trackers": [[1799.3023095674553, 373.7659292754023, 1977.167985492049, 909.3877957549861, 0.0],
+                          [1511.1885309670831, 392.01828807266287, 1752.7865371481664, 1118.8232391708957, 0.0],
+                          [1349.1707525520892, 428.2752206607351, 1550.9920214497497, 1035.7522250216414, 0.0],
+                          [1113.2440037497245, 420.2284774373579, 1171.7052187433192, 597.6110783613019, 0.0],
+                          [942.0050448808294, 441.3301280596905, 1033.4717423529773, 717.7302016822816, 0.0],
+                          [721.3794964644474, 423.8506853167762, 812.1704625821583, 698.2232689763503, 0.0],
+                          [1300.6714301980162, 339.8734833488548, 1578.3895405838223, 1175.0211782544206, 0.0],
+                          [572.819969641354, 452.79952595261244, 600.9965741918049, 539.3963281924049, 0.0],
+                          [546.6474850952587, 456.6081768371947, 573.6695149047413, 539.8028231628051, 0.0]],
+             "matched_indices": [[0, 2], [1, 5], [2, 1], [3, 0], [4, 8], [5, 6], [6, 7], [7, 4], [8, 3]],
+             "ret_unmatched_trackers": []})
+        test_and_expect(
+            {"trackers": [[1807.957767845859, 373.6235622731163, 1985.8250170484685, 909.2501664970832, 0.0],
+                          [1516.9136992167341, 392.13615724359005, 1758.5117053984409, 1118.9411083436999, 0.0],
+                          [1358.1033640428993, 424.6400589736668, 1565.3794317670943, 1048.4773535850961, 0.0],
+                          [1108.7269681917162, 425.86234198927093, 1164.94231545553, 596.5059129154392, 0.0],
+                          [944.4697182193237, 441.82942869881333, 1035.509938970747, 716.9499359186024, 0.0],
+                          [735.4283376555388, 427.2884090872653, 822.39063738044, 690.1778828813929, 0.0],
+                          [1303.9794013338467, 342.65700250926017, 1581.720616842016, 1177.8741786224264, 0.0],
+                          [572.4396109658231, 453.3202882687032, 599.9737948929811, 537.9422781373245, 0.0],
+                          [541.9631566574607, 465.4207993593844, 564.8879202656163, 536.090892948308, 0.0]],
+             "matched_indices": [[0, 2], [1, 5], [2, 1], [3, 0], [4, 3], [5, 7], [6, 4]],
+             "ret_unmatched_trackers": [6, 8]})
+
+    def test__filter_low_iou(self):
+        def test_and_expect(t):
+            kalman_tracker_obj = list(map(lambda x: DictObj(x), t['kalman_trackers']))
+            actual = association._filter_low_iou(
+                np.array(t['matched_indices']),
+                np.array(t['iou_matrix']),
+                t['iou_threshold'],
+                np.array(t['unmatched_detections']),
+                np.array(t['unmatched_trackers']),
+                kalman_tracker_obj
+            )
+            self.assertListEqual(list(map(lambda x: DictObj(x), t['ret_kalman_trackers'])), kalman_tracker_obj)
+            self.assertListEqual(t['ret_matches'], list(map(lambda x: x.tolist(), actual)))
+
+        test_and_expect({"matched_indices": [[0, 0], [1, 1], [3, 2]],
+                         "iou_matrix": [[0.8493698835372925, 0.0, 0.20014996826648712], [0.0, 0.9827505350112915, 0.0],
+                                        [0.0, 0.0, 0.0], [0.30149003863334656, 0.0, 0.843207061290741], [0.0, 0.0, 0.0],
+                                        [0.0, 0.0, 0.0]], "iou_threshold": 0.3, "unmatched_detections": [2, 4, 5],
+                         "unmatched_trackers": [],
+                         "kalman_trackers": [{"time_since_observed": 0, "confidence": 0.5, "age": 14},
+                                             {"time_since_observed": 0, "confidence": 0.5, "age": 14},
+                                             {"time_since_observed": 0, "confidence": 0.5, "age": 9}],
+                         "ret_kalman_trackers": [{"time_since_observed": 0, "confidence": 0.5, "age": 14},
+                                                 {"time_since_observed": 0, "confidence": 0.5, "age": 14},
+                                                 {"time_since_observed": 0, "confidence": 0.5, "age": 9}],
+                         "ret_matches": [[[0, 0]], [[1, 1]], [[3, 2]]]})
+        test_and_expect({"matched_indices": [[0, 0], [1, 1], [4, 2]],
+                         "iou_matrix": [[0.8876456618309021, 0.0, 0.21911640465259552], [0.0, 0.962422251701355, 0.0],
+                                        [0.0, 0.0, 0.0], [0.0, 0.0, 0.0],
+                                        [0.2594248950481415, 0.0, 0.8539206385612488]], "iou_threshold": 0.3,
+                         "unmatched_detections": [2, 3], "unmatched_trackers": [],
+                         "kalman_trackers": [{"time_since_observed": 0, "confidence": 0.5, "age": 13},
+                                             {"time_since_observed": 0, "confidence": 0.5, "age": 13},
+                                             {"time_since_observed": 0, "confidence": 0.5, "age": 8}],
+                         "ret_kalman_trackers": [{"time_since_observed": 0, "confidence": 0.5, "age": 13},
+                                                 {"time_since_observed": 0, "confidence": 0.5, "age": 13},
+                                                 {"time_since_observed": 0, "confidence": 0.5, "age": 8}],
+                         "ret_matches": [[[0, 0]], [[1, 1]], [[4, 2]]]})
+        test_and_expect({"matched_indices": [[0, 0], [1, 1]],
+                         "iou_matrix": [[0.6649591326713562, 0.0], [0.0, 0.5956258177757263], [0.0, 0.0]],
+                         "iou_threshold": 0.3, "unmatched_detections": [2], "unmatched_trackers": [],
+                         "kalman_trackers": [{"time_since_observed": 0, "confidence": 0.5, "age": 3},
+                                             {"time_since_observed": 0, "confidence": 0.5, "age": 3}],
+                         "ret_kalman_trackers": [{"time_since_observed": 0, "confidence": 0.5, "age": 3},
+                                                 {"time_since_observed": 0, "confidence": 0.5, "age": 3}],
+                         "ret_matches": [[[0, 0]], [[1, 1]]]})
+
+    def test__clean_up_to_delete(self):
+        with open('spec/res/associate__clean_up_to_delete.json') as f:
+            invocations = json.load(f)
+
+            i = 0
+            for x in invocations:
+                matches, unmatched_trackers, unmatched_detections = association._clean_up_to_delete(
+                    x['matches_ext_com'], np.array(x['unmatched_detections']), np.array(x['unmatched_trackers']),
+                    x['matches'], x['unmatched_before'])
+
+                self.assertListEqual(x['ret_matches'], matches.tolist())
+                self.assertListEqual(x['ret_unmatched_detections'], unmatched_detections.tolist())
+                self.assertListEqual(x['ret_unmatched_trackers'],
+                                     unmatched_trackers if isinstance(unmatched_trackers,
+                                                                      list) else unmatched_trackers.tolist())
+
+                i += 1
+
+    def test__occluded_trackers(self):
+        with open('spec/res/association__occluded_tracker.json') as f:
+            invocations = json.load(f)
+
+            i = 0
+            for x in invocations:
+                kalman_tracker_obj = list(map(lambda t: DictObj(t), x['kalman_trackers']))
+                occluded_trackers, unmatched_trackers = association._occluded_trackers(
+                    x['frame_count'], x['min_hits'], np.array(x['ios_matrix']), np.array(x['unmatched_trackers']),
+                    np.array(x['trackers']), kalman_tracker_obj, x['average_area'], x['conf_trgt'], x['conf_objt'])
+
+                self.assertListEqual(x['ret_occluded_trackers'], occluded_trackers)
+                self.assertListEqual(x['ret_unmatched_trackers'],
+                                     unmatched_trackers if isinstance(unmatched_trackers,
+                                                                      list) else unmatched_trackers.tolist())
+                expected_lists = list(map(lambda t: DictObj(t), x['ret_kalman_trackers']))
+                self.assertEqual(len(kalman_tracker_obj), len(expected_lists))
+                for i in range(len(expected_lists)):
+                    self.assertTrue(expected_lists[i], kalman_tracker_obj[i])
+
+                i += 1
+
+    def test__build_iou_matrix_ext(self):
+        with open('spec/res/association__build_iou_matrix_ext.json') as f:
+            invocations = json.load(f)
+            for x in invocations:
+                kalman_tracker_obj = list(map(lambda t: DictObj(t), x['kalman_trackers']))
+                iou_matrix_ext = association._build_iou_matrix_ext(
+                    np.array(x['unmatched_trackers']),
+                    np.array(x['unmatched_detections']),
+                    np.array(x['detections']),
+                    np.array(x['trackers']),
+                    kalman_tracker_obj
+                )
+                self.assertListEqual(x['ret_iou_matrix_ext'], iou_matrix_ext.tolist())
+
+    def test__filter_low_iou_low_area(self):
+        with open('spec/res/association__filter_low_iou_low_area.json') as f:
+            invocations = json.load(f)
+            for x in invocations:
+                matches_ext_com, del_ind = association._filter_low_iou_low_area(
+                    np.array(x['matched_indices_ext']),
+                    np.array(x['matched_indices']),
+                    np.array(x['iou_matrix_ext']),
+                    x['iou_threshold'],
+                    np.array(x['iou_matrix'])
+                )
+
+                if len(matches_ext_com) > 0:
+                    if isinstance(matches_ext_com[0], list):
+                        self.assertListEqual(x['ret_matches_ext_com'], matches_ext_com)
+                    else:
+                        self.assertListEqual(x['ret_matches_ext_com'], list(map(lambda l: l.tolist(), matches_ext_com)))
+                else:
+                    self.assertListEqual(x['ret_matches_ext_com'], matches_ext_com)
+                self.assertListEqual(x['ret_del_ind'], del_ind.tolist())
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/spec/libs/test_association_find_new_trackers_2.py b/spec/libs/test_association_find_new_trackers_2.py
new file mode 100644
index 0000000..6a7e5b8
--- /dev/null
+++ b/spec/libs/test_association_find_new_trackers_2.py
@@ -0,0 +1,127 @@
+import unittest
+import numpy as np
+from libs import association
+
+class TestAssociation(unittest.TestCase):
+    def test_find_new_trackers_2(self):
+        test_data = [
+            {"detections": [[1480.3, 413.27, 1600.56, 776.04, 0.87614]], "detections_before": [[1480.3, 413.27, 1600.56, 776.04, 0.80295], [643.66, 461.78, 703.289, 642.67, 0.74373]], "detections_before_before": [[1480.3, 413.27, 1600.56, 776.04, 0.65959]], "average_area": [33416.426909973314], "iou_threshold": 0.3, "iteration": 0, "return_matches": [[0, 0, 0]], "return_del_ind": [[0, 0, 0]]},
+            {"detections": [[1254.6, 446.72, 1288.422, 550.19, 0.52236], [729.81, 446.86, 771.6809999999999, 574.47, 0.39156], [460.48, 442.1, 551.376, 716.79, 0.33475]], "detections_before": [[729.81, 446.86, 771.6809999999999, 574.47, 0.45047], [1255.0, 441.39, 1291.321, 552.35, 0.39539], [454.06, 434.36, 551.552, 728.83, 0.36475]], "detections_before_before": [[454.06, 434.36, 551.552, 728.83, 0.48616], [1254.6, 446.72, 1288.422, 550.19, 0.35682], [657.86, 419.0, 731.503, 641.9300000000001, 0.32016]], "average_area": [41899.645008540414], "iou_threshold": 0.3, "iteration": 1, "return_matches": [[0, 1, 1], [2, 2, 0]], "return_del_ind": [[0, 1, 1], [2, 2, 0]]},
+            {"detections": [[460.48, 442.1, 551.376, 716.79, 0.51467], [1254.6, 446.72, 1288.422, 550.19, 0.44909], [729.0, 457.0, 768.0, 576.0, 0.36282]], "detections_before": [[1254.6, 446.72, 1288.422, 550.19, 0.52236], [729.81, 446.86, 771.6809999999999, 574.47, 0.39156], [460.48, 442.1, 551.376, 716.79, 0.33475]], "detections_before_before": [[729.81, 446.86, 771.6809999999999, 574.47, 0.45047], [1255.0, 441.39, 1291.321, 552.35, 0.39539], [454.06, 434.36, 551.552, 728.83, 0.36475]], "average_area": [43632.14584964385], "iou_threshold": 0.3, "iteration": 2, "return_matches": [[0, 2, 2], [1, 0, 1], [2, 1, 0]], "return_del_ind": [[0, 2, 2], [1, 0, 1], [2, 1, 0]]},
+            {"detections": [[729.0, 449.0, 768.0, 568.0, 0.57287], [1254.6, 446.72, 1288.422, 550.19, 0.51015], [460.48, 442.1, 551.376, 716.79, 0.49628]], "detections_before": [[460.48, 442.1, 551.376, 716.79, 0.51467], [1254.6, 446.72, 1288.422, 550.19, 0.44909], [729.0, 457.0, 768.0, 576.0, 0.36282]], "detections_before_before": [[1254.6, 446.72, 1288.422, 550.19, 0.52236], [729.81, 446.86, 771.6809999999999, 574.47, 0.39156], [460.48, 442.1, 551.376, 716.79, 0.33475]], "average_area": [42257.88713731571], "iou_threshold": 0.3, "iteration": 3, "return_matches": [[0, 2, 1], [1, 1, 0], [2, 0, 2]], "return_del_ind": [[0, 2, 1], [1, 1, 0], [2, 0, 2]]},
+            {"detections": [[1255.1, 449.36, 1286.59, 545.83, 0.73431], [729.0, 449.0, 768.0, 568.0, 0.57074]], "detections_before": [[729.0, 449.0, 768.0, 568.0, 0.57287], [1254.6, 446.72, 1288.422, 550.19, 0.51015], [460.48, 442.1, 551.376, 716.79, 0.49628]], "detections_before_before": [[460.48, 442.1, 551.376, 716.79, 0.51467], [1254.6, 446.72, 1288.422, 550.19, 0.44909], [729.0, 457.0, 768.0, 576.0, 0.36282]], "average_area": [40780.42096181836], "iou_threshold": 0.3, "iteration": 4, "return_matches": [[0, 1, 1], [1, 0, 2]], "return_del_ind": [[0, 1, 1], [1, 0, 2]]},
+            {"detections": [[729.0, 449.0, 768.0, 568.0, 0.55511], [1254.6, 446.72, 1288.422, 550.19, 0.39739], [478.71, 493.64, 552.353, 716.5699999999999, 0.33771]], "detections_before": [[1255.1, 449.36, 1286.59, 545.83, 0.73431], [729.0, 449.0, 768.0, 568.0, 0.57074]], "detections_before_before": [[729.0, 449.0, 768.0, 568.0, 0.57287], [1254.6, 446.72, 1288.422, 550.19, 0.51015], [460.48, 442.1, 551.376, 716.79, 0.49628]], "average_area": [41242.063343166206], "iou_threshold": 0.3, "iteration": 5, "return_matches": [[0, 1, 0], [1, 0, 1]], "return_del_ind": [[0, 1, 0], [1, 0, 1]]},
+            {"detections": [[1254.6, 446.72, 1288.422, 550.19, 0.5207], [729.81, 446.86, 771.6809999999999, 574.47, 0.49008]], "detections_before": [[729.0, 449.0, 768.0, 568.0, 0.55511], [1254.6, 446.72, 1288.422, 550.19, 0.39739], [478.71, 493.64, 552.353, 716.5699999999999, 0.33771]], "detections_before_before": [[1255.1, 449.36, 1286.59, 545.83, 0.73431], [729.0, 449.0, 768.0, 568.0, 0.57074]], "average_area": [44227.14630929491], "iou_threshold": 0.3, "iteration": 6, "return_matches": [[0, 1, 0], [1, 0, 1]], "return_del_ind": [[0, 1, 0], [1, 0, 1]]},
+            {"detections": [[1254.6, 446.72, 1288.422, 550.19, 0.9907], [729.81, 446.86, 771.6809999999999, 574.47, 0.52778], [481.15, 464.01, 565.8919999999999, 720.24, 0.45687]], "detections_before": [[1254.6, 446.72, 1288.422, 550.19, 0.5207], [729.81, 446.86, 771.6809999999999, 574.47, 0.49008]], "detections_before_before": [[729.0, 449.0, 768.0, 568.0, 0.55511], [1254.6, 446.72, 1288.422, 550.19, 0.39739], [478.71, 493.64, 552.353, 716.5699999999999, 0.33771]], "average_area": [44914.9080941074], "iou_threshold": 0.3, "iteration": 7, "return_matches": [[0, 0, 1], [1, 1, 0]], "return_del_ind": [[0, 0, 1], [1, 1, 0]]},
+            {"detections": [[1254.6, 446.72, 1288.422, 550.19, 0.89436], [478.86, 460.48, 569.756, 735.1700000000001, 0.48132], [729.81, 446.86, 771.6809999999999, 574.47, 0.33441]], "detections_before": [[1254.6, 446.72, 1288.422, 550.19, 0.9907], [729.81, 446.86, 771.6809999999999, 574.47, 0.52778], [481.15, 464.01, 565.8919999999999, 720.24, 0.45687]], "detections_before_before": [[1254.6, 446.72, 1288.422, 550.19, 0.5207], [729.81, 446.86, 771.6809999999999, 574.47, 0.49008]], "average_area": [46961.41488824861], "iou_threshold": 0.3, "iteration": 8, "return_matches": [[0, 0, 0], [2, 1, 1]], "return_del_ind": [[0, 0, 0], [2, 1, 1]]},
+            {"detections": [[465.47, 444.35, 570.03, 760.03, 0.53576]], "detections_before": [[1254.6, 446.72, 1288.422, 550.19, 0.89436], [478.86, 460.48, 569.756, 735.1700000000001, 0.48132]], "detections_before_before": [[1254.6, 446.72, 1288.422, 550.19, 0.9907], [481.15, 464.01, 565.8919999999999, 720.24, 0.45687]], "average_area": [35233.49032028802], "iou_threshold": 0.3, "iteration": 9, "return_matches": [[0, 1, 1]], "return_del_ind": [[0, 1, 1]]},
+            {"detections": [[465.47, 444.35, 570.03, 760.03, 0.5274]], "detections_before": [[465.47, 444.35, 570.03, 760.03, 0.53576]], "detections_before_before": [[1254.6, 446.72, 1288.422, 550.19, 0.89436], [478.86, 460.48, 569.756, 735.1700000000001, 0.48132]], "average_area": [33987.1930667672], "iou_threshold": 0.3, "iteration": 10, "return_matches": [[0, 0, 1]], "return_del_ind": [[0, 0, 1]]},
+            {"detections": [[465.47, 444.35, 570.03, 760.03, 0.69666]], "detections_before": [[465.47, 444.35, 570.03, 760.03, 0.5274]], "detections_before_before": [[465.47, 444.35, 570.03, 760.03, 0.53576]], "average_area": [34807.352144271434], "iou_threshold": 0.3, "iteration": 11, "return_matches": [[0, 0, 0]], "return_del_ind": [[0, 0, 0]]},
+            {"detections": [[473.76, 454.06, 571.252, 748.53, 0.75093]], "detections_before": [[465.47, 444.35, 570.03, 760.03, 0.69666]], "detections_before_before": [[465.47, 444.35, 570.03, 760.03, 0.5274]], "average_area": [34988.46766943957], "iou_threshold": 0.3, "iteration": 12, "return_matches": [[0, 0, 0]], "return_del_ind": [[0, 0, 0]]},
+            {"detections": [[478.86, 442.1, 569.756, 716.79, 1.1515]], "detections_before": [[473.76, 454.06, 571.252, 748.53, 0.75093]], "detections_before_before": [[465.47, 444.35, 570.03, 760.03, 0.69666]], "average_area": [32697.497424139863], "iou_threshold": 0.3, "iteration": 13, "return_matches": [[0, 0, 0]], "return_del_ind": [[0, 0, 0]]},
+            {"detections": [[721.23, 455.43, 763.101, 583.04, 0.64348]], "detections_before": [[721.23, 455.43, 763.101, 583.04, 0.57398]], "detections_before_before": [[721.23, 455.43, 763.101, 583.04, 0.39383]], "average_area": [38227.43009144287], "iou_threshold": 0.3, "iteration": 14, "return_matches": [[0, 0, 0]], "return_del_ind": [[0, 0, 0]]},
+            {"detections": [[721.23, 455.43, 763.101, 583.04, 0.3272]], "detections_before": [[721.23, 455.43, 763.101, 583.04, 0.64348]], "detections_before_before": [[721.23, 455.43, 763.101, 583.04, 0.57398]], "average_area": [37521.95254768199], "iou_threshold": 0.3, "iteration": 15, "return_matches": [[0, 0, 0]], "return_del_ind": [[0, 0, 0]]},
+            {"detections": [[721.0, 457.0, 760.0, 576.0, 0.61912]], "detections_before": [[721.23, 455.43, 763.101, 583.04, 0.3272]], "detections_before_before": [[721.23, 455.43, 763.101, 583.04, 0.64348]], "average_area": [37933.0836814937], "iou_threshold": 0.3, "iteration": 16, "return_matches": [[0, 0, 0]], "return_del_ind": [[0, 0, 0]]},
+            {"detections": [[721.0, 457.0, 760.0, 576.0, 0.34328]], "detections_before": [[721.0, 457.0, 760.0, 576.0, 0.61912]], "detections_before_before": [[721.23, 455.43, 763.101, 583.04, 0.3272]], "average_area": [36387.005629979845], "iou_threshold": 0.3, "iteration": 17, "return_matches": [[0, 0, 0]], "return_del_ind": [[0, 0, 0]]},
+            {"detections": [[1450.0, 429.71, 1491.871, 557.3199999999999, 0.9037]], "detections_before": [[1443.8, 432.91, 1488.748, 569.75, 0.70279], [601.19, 446.86, 685.932, 703.09, 1.2662]], "detections_before_before": [[1441.5, 429.71, 1483.371, 557.3199999999999, 0.34033]], "average_area": [40794.528893818206], "iou_threshold": 0.3, "iteration": 18, "return_matches": [[0, 0, 0]], "return_del_ind": [[0, 0, 0]]},
+            {"detections": [[672.78, 433.93, 746.423, 656.86, 0.43969], [1450.0, 429.71, 1491.871, 557.3199999999999, 1.0982]], "detections_before": [[1450.0, 429.71, 1491.871, 557.3199999999999, 0.9037]], "detections_before_before": [[1443.8, 432.91, 1488.748, 569.75, 0.70279], [601.19, 446.86, 685.932, 703.09, 1.2662]], "average_area": [38725.18380509759], "iou_threshold": 0.3, "iteration": 19, "return_matches": [[1, 0, 0]], "return_del_ind": [[1, 0, 0]]},
+            {"detections": [[680.04, 461.78, 739.669, 642.67, 0.5207]], "detections_before": [[676.79, 455.86, 740.77, 649.8, 0.80057], [1605.5, 296.57, 1815.62, 928.9300000000001, 0.32427]], "detections_before_before": [[672.78, 433.93, 746.423, 656.86, 0.43969]], "average_area": [31188.62597979776], "iou_threshold": 0.3, "iteration": 20, "return_matches": [[0, 0, 0]], "return_del_ind": [[0, 0, 0]]},
+            {"detections": [[598.82, 364.89, 727.7800000000001, 753.77, 0.45824], [680.04, 461.78, 739.669, 642.67, 0.64739]], "detections_before": [[680.04, 461.78, 739.669, 642.67, 0.5207]], "detections_before_before": [[676.79, 455.86, 740.77, 649.8, 0.80057], [1605.5, 296.57, 1815.62, 928.9300000000001, 0.32427]], "average_area": [31427.564092942466], "iou_threshold": 0.3, "iteration": 21, "return_matches": [[1, 0, 0]], "return_del_ind": [[1, 0, 0]]},
+            {"detections": [[566.69, 419.61, 622.259, 588.32, 0.33601]], "detections_before": [[598.82, 364.89, 727.7800000000001, 753.77, 0.45824], [680.04, 461.78, 739.669, 642.67, 0.64739]], "detections_before_before": [[680.04, 461.78, 739.669, 642.67, 0.5207]], "average_area": [31042.91497519174], "iou_threshold": 0.3, "iteration": 22, "return_matches": [], "return_del_ind": []},
+            {"detections": [[689.79, 455.86, 753.77, 649.8, 0.35075]], "detections_before": [[579.94, 451.29, 624.888, 588.13, 0.43089]], "detections_before_before": [[579.94, 451.29, 624.888, 588.13, 0.48921]], "average_area": [19744.05829019198], "iou_threshold": 0.3, "iteration": 23, "return_matches": [], "return_del_ind": []},
+            {"detections": [[578.0, 430.92, 633.569, 599.63, 0.34068]], "detections_before": [[579.94, 451.29, 624.888, 588.13, 0.49629]], "detections_before_before": [[1261.6, 446.72, 1295.422, 550.19, 0.63399], [579.94, 451.29, 624.888, 588.13, 0.70894]], "average_area": [28603.579354949135], "iou_threshold": 0.3, "iteration": 24, "return_matches": [[0, 0, 1]], "return_del_ind": [[0, 0, 1]]},
+            {"detections": [[687.71, 463.78, 761.3530000000001, 686.71, 0.41933]], "detections_before": [[687.71, 463.78, 761.3530000000001, 686.71, 0.44973]], "detections_before_before": [[697.44, 460.65, 766.0840000000001, 668.5799999999999, 0.30155]], "average_area": [29521.063616823514], "iou_threshold": 0.3, "iteration": 25, "return_matches": [[0, 0, 0]], "return_del_ind": [[0, 0, 0]]},
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+            {"detections": [[532.85, 454.06, 556.4730000000001, 526.929, 0.42961]], "detections_before": [[534.0, 460.48, 555.974, 528.402, 0.54386]], "detections_before_before": [[534.0, 460.48, 555.974, 528.402, 0.7366]], "average_area": [31360.703525707064], "iou_threshold": 0.3, "iteration": 35, "return_matches": [[0, 0, 0]], "return_del_ind": [[0, 0, 0]]},
+            {"detections": [[534.0, 460.48, 555.974, 528.402, 0.78249]], "detections_before": [[532.85, 454.06, 556.4730000000001, 526.929, 0.42961]], "detections_before_before": [[534.0, 460.48, 555.974, 528.402, 0.54386]], "average_area": [30832.359653102012], "iou_threshold": 0.3, "iteration": 36, "return_matches": [[0, 0, 0]], "return_del_ind": [[0, 0, 0]]},
+            {"detections": [[534.0, 460.48, 555.974, 528.402, 1.1785]], "detections_before": [[534.0, 460.48, 555.974, 528.402, 0.78249]], "detections_before_before": [[532.85, 454.06, 556.4730000000001, 526.929, 0.42961]], "average_area": [31846.285434349862], "iou_threshold": 0.3, "iteration": 37, "return_matches": [[0, 0, 0]], "return_del_ind": [[0, 0, 0]]},
+            {"detections": [[780.76, 338.9, 909.72, 727.78, 0.34854]], "detections_before": [[780.76, 338.9, 909.72, 727.78, 0.59085]], "detections_before_before": [[780.76, 338.9, 909.72, 727.78, 0.38443]], "average_area": [28492.686209752963], "iou_threshold": 0.3, "iteration": 38, "return_matches": [[0, 0, 0]], "return_del_ind": [[0, 0, 0]]},
+            {"detections": [[780.76, 338.9, 909.72, 727.78, 0.3336]], "detections_before": [[780.76, 338.9, 909.72, 727.78, 0.34854]], "detections_before_before": [[780.76, 338.9, 909.72, 727.78, 0.59085]], "average_area": [29257.66140513454], "iou_threshold": 0.3, "iteration": 39, "return_matches": [[0, 0, 0]], "return_del_ind": [[0, 0, 0]]},
+            {"detections": [[673.0, 321.0, 832.0, 800.0, 0.44092]], "detections_before": [[524.81, 455.88, 546.784, 523.802, 0.66566]], "detections_before_before": [[529.4, 460.48, 551.374, 528.402, 0.39138], [1212.8, 446.72, 1246.6219999999998, 550.19, 0.31598]], "average_area": [40305.19371358487], "iou_threshold": 0.3, "iteration": 40, "return_matches": [], "return_del_ind": []},
+            {"detections": [[524.81, 455.88, 546.784, 523.802, 0.52751]], "detections_before": [[673.0, 321.0, 832.0, 800.0, 0.44092]], "detections_before_before": [[524.81, 455.88, 546.784, 523.802, 0.66566]], "average_area": [42995.54322102978], "iou_threshold": 0.3, "iteration": 41, "return_matches": [], "return_del_ind": []},
+            {"detections": [[1209.6, 449.36, 1241.09, 545.83, 0.31801]], "detections_before": [[524.81, 455.88, 546.784, 523.802, 0.52751]], "detections_before_before": [[673.0, 321.0, 832.0, 800.0, 0.44092]], "average_area": [42189.70037304218], "iou_threshold": 0.3, "iteration": 42, "return_matches": [], "return_del_ind": []},
+            {"detections": [[1205.8, 446.72, 1239.6219999999998, 550.19, 0.48665], [497.0, 449.0, 536.0, 568.0, 0.38349], [728.78, 390.88, 857.74, 779.76, 0.35683]], "detections_before": [[1209.6, 449.36, 1241.09, 545.83, 0.31801]], "detections_before_before": [[524.81, 455.88, 546.784, 523.802, 0.52751]], "average_area": [41879.3692563351], "iou_threshold": 0.3, "iteration": 43, "return_matches": [], "return_del_ind": []},
+            {"detections": [[1209.6, 449.36, 1241.09, 545.83, 0.50404], [498.3, 446.86, 540.171, 574.47, 0.39774], [717.57, 329.43, 865.86, 776.29, 0.42003]], "detections_before": [[1205.8, 446.72, 1239.6219999999998, 550.19, 0.48665], [497.0, 449.0, 536.0, 568.0, 0.38349], [728.78, 390.88, 857.74, 779.76, 0.35683]], "detections_before_before": [[1209.6, 449.36, 1241.09, 545.83, 0.31801]], "average_area": [39239.14569611801], "iou_threshold": 0.3, "iteration": 44, "return_matches": [[0, 0, 0]], "return_del_ind": [[0, 0, 0]]},
+            {"detections": [[717.57, 329.43, 865.86, 776.29, 0.57229], [497.0, 449.0, 536.0, 568.0, 0.3493]], "detections_before": [[1209.6, 449.36, 1241.09, 545.83, 0.50404], [498.3, 446.86, 540.171, 574.47, 0.39774], [717.57, 329.43, 865.86, 776.29, 0.42003]], "detections_before_before": [[1205.8, 446.72, 1239.6219999999998, 550.19, 0.48665], [497.0, 449.0, 536.0, 568.0, 0.38349], [728.78, 390.88, 857.74, 779.76, 0.35683]], "average_area": [39836.0147023516], "iou_threshold": 0.3, "iteration": 45, "return_matches": [[0, 2, 2], [1, 1, 1]], "return_del_ind": [[0, 2, 2], [1, 1, 1]]},
+            {"detections": [[705.0, 321.0, 864.0, 800.0, 0.60604], [497.0, 449.0, 536.0, 568.0, 0.56005]], "detections_before": [[717.57, 329.43, 865.86, 776.29, 0.57229], [497.0, 449.0, 536.0, 568.0, 0.3493]], "detections_before_before": [[1209.6, 449.36, 1241.09, 545.83, 0.50404], [498.3, 446.86, 540.171, 574.47, 0.39774], [717.57, 329.43, 865.86, 776.29, 0.42003]], "average_area": [39378.71188770526], "iou_threshold": 0.3, "iteration": 46, "return_matches": [[0, 0, 2], [1, 1, 1]], "return_del_ind": [[0, 0, 2], [1, 1, 1]]},
+            {"detections": [[498.3, 446.86, 540.171, 574.47, 0.70425]], "detections_before": [[705.0, 321.0, 864.0, 800.0, 0.60604], [497.0, 449.0, 536.0, 568.0, 0.56005]], "detections_before_before": [[717.57, 329.43, 865.86, 776.29, 0.57229], [497.0, 449.0, 536.0, 568.0, 0.3493]], "average_area": [41126.6642030188], "iou_threshold": 0.3, "iteration": 47, "return_matches": [[0, 1, 1]], "return_del_ind": [[0, 1, 1]]},
+            {"detections": [[497.0, 449.0, 536.0, 568.0, 0.62453], [875.57, 429.71, 960.312, 685.94, 0.53376], [705.0, 321.0, 864.0, 800.0, 0.49236]], "detections_before": [[498.3, 446.86, 540.171, 574.47, 0.70425]], "detections_before_before": [[705.0, 321.0, 864.0, 800.0, 0.60604], [497.0, 449.0, 536.0, 568.0, 0.56005]], "average_area": [42985.86866180526], "iou_threshold": 0.3, "iteration": 48, "return_matches": [[0, 0, 1]], "return_del_ind": [[0, 0, 1]]},
+            {"detections": [[497.0, 449.0, 536.0, 568.0, 1.118], [705.0, 321.0, 864.0, 800.0, 0.48164]], "detections_before": [[497.0, 449.0, 536.0, 568.0, 0.62453], [875.57, 429.71, 960.312, 685.94, 0.53376], [705.0, 321.0, 864.0, 800.0, 0.49236]], "detections_before_before": [[498.3, 446.86, 540.171, 574.47, 0.70425]], "average_area": [43192.34384876516], "iou_threshold": 0.3, "iteration": 49, "return_matches": [[0, 0, 0]], "return_del_ind": [[0, 0, 0]]},
+            {"detections": [[892.72, 429.71, 977.462, 685.94, 0.60489], [705.0, 321.0, 864.0, 800.0, 0.36742]], "detections_before": [[705.0, 321.0, 864.0, 800.0, 0.48164]], "detections_before_before": [[875.57, 429.71, 960.312, 685.94, 0.53376], [705.0, 321.0, 864.0, 800.0, 0.49236]], "average_area": [33611.445346253226], "iou_threshold": 0.3, "iteration": 50, "return_matches": [[1, 0, 1]], "return_del_ind": [[1, 0, 1]]},
+            {"detections": [[892.72, 429.71, 977.462, 685.94, 1.0319]], "detections_before": [[892.72, 429.71, 977.462, 685.94, 0.60489], [705.0, 321.0, 864.0, 800.0, 0.36742]], "detections_before_before": [[705.0, 321.0, 864.0, 800.0, 0.48164]], "average_area": [33631.30541697515], "iou_threshold": 0.3, "iteration": 51, "return_matches": [], "return_del_ind": []},
+            {"detections": [[892.72, 429.71, 977.462, 685.94, 0.70477]], "detections_before": [[892.72, 429.71, 977.462, 685.94, 1.0319]], "detections_before_before": [[892.72, 429.71, 977.462, 685.94, 0.60489], [705.0, 321.0, 864.0, 800.0, 0.36742]], "average_area": [37881.73192474145], "iou_threshold": 0.3, "iteration": 52, "return_matches": [[0, 0, 0]], "return_del_ind": [[0, 0, 0]]},
+            {"detections": [[938.34, 423.72, 1029.236, 698.4100000000001, 0.41726], [469.67, 451.29, 514.618, 588.13, 0.40819], [505.0, 449.0, 544.0, 568.0, 0.51051]], "detections_before": [[938.34, 423.72, 1029.236, 698.4100000000001, 0.68982]], "detections_before_before": [[858.74, 364.89, 987.7, 753.77, 0.6961]], "average_area": [62483.30821791463], "iou_threshold": 0.3, "iteration": 53, "return_matches": [], "return_del_ind": []},
+            {"detections": [[505.0, 449.0, 544.0, 568.0, 0.3979], [464.01, 455.43, 505.881, 583.04, 0.35908], [1149.9, 429.71, 1191.7710000000002, 557.3199999999999, 0.30725]], "detections_before": [[469.67, 451.29, 514.618, 588.13, 0.40819], [505.0, 449.0, 544.0, 568.0, 0.51051]], "detections_before_before": [[938.34, 423.72, 1029.236, 698.4100000000001, 0.68982]], "average_area": [64174.110696734395], "iou_threshold": 0.3, "iteration": 54, "return_matches": [], "return_del_ind": []},
+            {"detections": [[464.01, 455.43, 505.881, 583.04, 0.34395], [737.0, 353.0, 896.0, 832.0, 0.40197]], "detections_before": [[505.0, 449.0, 544.0, 568.0, 0.3979], [464.01, 455.43, 505.881, 583.04, 0.35908], [1149.9, 429.71, 1191.7710000000002, 557.3199999999999, 0.30725]], "detections_before_before": [[469.67, 451.29, 514.618, 588.13, 0.40819], [505.0, 449.0, 544.0, 568.0, 0.51051]], "average_area": [56511.03043192209], "iou_threshold": 0.3, "iteration": 55, "return_matches": [[0, 1, 0]], "return_del_ind": [[0, 1, 0]]},
+            {"detections": [[513.0, 449.0, 552.0, 568.0, 0.37197]], "detections_before": [[464.01, 455.43, 505.881, 583.04, 0.34395], [737.0, 353.0, 896.0, 832.0, 0.40197]], "detections_before_before": [[505.0, 449.0, 544.0, 568.0, 0.3979], [464.01, 455.43, 505.881, 583.04, 0.35908], [1149.9, 429.71, 1191.7710000000002, 557.3199999999999, 0.30725]], "average_area": [55229.5371756217], "iou_threshold": 0.3, "iteration": 56, "return_matches": [], "return_del_ind": []},
+            {"detections": [[505.0, 449.0, 544.0, 568.0, 0.39243]], "detections_before": [[513.0, 449.0, 552.0, 568.0, 0.37197]], "detections_before_before": [[464.01, 455.43, 505.881, 583.04, 0.34395], [737.0, 353.0, 896.0, 832.0, 0.40197]], "average_area": [54724.16330952654], "iou_threshold": 0.3, "iteration": 57, "return_matches": [], "return_del_ind": []},
+            {"detections": [[755.53, 309.67, 926.01, 823.1200000000001, 0.46075]], "detections_before": [[769.0, 353.0, 928.0, 832.0, 0.37895]], "detections_before_before": [[769.0, 353.0, 928.0, 832.0, 0.54118]], "average_area": [49865.200217524354], "iou_threshold": 0.3, "iteration": 58, "return_matches": [[0, 0, 0]], "return_del_ind": [[0, 0, 0]]},
+            {"detections": [[1153.0, 433.0, 1192.0, 552.0, 0.48299]], "detections_before": [[755.53, 309.67, 926.01, 823.1200000000001, 0.46075]], "detections_before_before": [[769.0, 353.0, 928.0, 832.0, 0.37895]], "average_area": [51701.48161951506], "iou_threshold": 0.3, "iteration": 59, "return_matches": [], "return_del_ind": []},
+            {"detections": [[1149.9, 429.71, 1191.7710000000002, 557.3199999999999, 0.31112], [755.53, 309.67, 926.01, 823.1200000000001, 0.50555]], "detections_before": [[1153.0, 433.0, 1192.0, 552.0, 0.48299]], "detections_before_before": [[755.53, 309.67, 926.01, 823.1200000000001, 0.46075]], "average_area": [52711.85334512396], "iou_threshold": 0.3, "iteration": 60, "return_matches": [], "return_del_ind": []},
+            {"detections": [[572.25, 454.06, 620.496, 600.8, 0.42117], [1153.0, 433.0, 1192.0, 552.0, 0.41303], [755.53, 343.97, 926.01, 857.4200000000001, 0.59531]], "detections_before": [[1149.9, 429.71, 1191.7710000000002, 557.3199999999999, 0.31112], [755.53, 309.67, 926.01, 823.1200000000001, 0.50555]], "detections_before_before": [[1153.0, 433.0, 1192.0, 552.0, 0.48299]], "average_area": [48711.517890507304], "iou_threshold": 0.3, "iteration": 61, "return_matches": [[1, 0, 0]], "return_del_ind": [[1, 0, 0]]},
+            {"detections": [[572.25, 454.06, 620.496, 600.8, 0.39443], [505.0, 449.0, 544.0, 568.0, 0.34845], [1153.0, 433.0, 1192.0, 552.0, 0.56941]], "detections_before": [[572.25, 454.06, 620.496, 600.8, 0.42117], [1153.0, 433.0, 1192.0, 552.0, 0.41303], [755.53, 343.97, 926.01, 857.4200000000001, 0.59531]], "detections_before_before": [[1149.9, 429.71, 1191.7710000000002, 557.3199999999999, 0.31112], [755.53, 309.67, 926.01, 823.1200000000001, 0.50555]], "average_area": [47885.52422544819], "iou_threshold": 0.3, "iteration": 62, "return_matches": [[2, 1, 0]], "return_del_ind": [[2, 1, 0]]},
+            {"detections": [[1153.0, 433.0, 1192.0, 552.0, 0.31442], [896.71, 329.43, 1045.0, 776.29, 0.31669]], "detections_before": [[572.25, 454.06, 620.496, 600.8, 0.39443], [505.0, 449.0, 544.0, 568.0, 0.34845], [1153.0, 433.0, 1192.0, 552.0, 0.56941]], "detections_before_before": [[572.25, 454.06, 620.496, 600.8, 0.42117], [1153.0, 433.0, 1192.0, 552.0, 0.41303], [755.53, 343.97, 926.01, 857.4200000000001, 0.59531]], "average_area": [51242.19921113908], "iou_threshold": 0.3, "iteration": 63, "return_matches": [[0, 2, 1]], "return_del_ind": [[0, 2, 1]]},
+            {"detections": [[571.03, 454.91, 622.81, 612.25, 0.31746], [505.0, 441.0, 544.0, 560.0, 0.37783]], "detections_before": [[1153.0, 433.0, 1192.0, 552.0, 0.31442], [896.71, 329.43, 1045.0, 776.29, 0.31669]], "detections_before_before": [[572.25, 454.06, 620.496, 600.8, 0.39443], [505.0, 449.0, 544.0, 568.0, 0.34845], [1153.0, 433.0, 1192.0, 552.0, 0.56941]], "average_area": [52520.60098107217], "iou_threshold": 0.3, "iteration": 64, "return_matches": [], "return_del_ind": []},
+            {"detections": [[566.69, 453.55, 622.259, 622.26, 0.551], [1149.9, 429.71, 1191.7710000000002, 557.3199999999999, 0.86818]], "detections_before": [[571.03, 454.91, 622.81, 612.25, 0.53513]], "detections_before_before": [[571.03, 454.91, 622.81, 612.25, 0.42003]], "average_area": [51055.32089337418], "iou_threshold": 0.3, "iteration": 65, "return_matches": [[0, 0, 0]], "return_del_ind": [[0, 0, 0]]},
+            {"detections": [[789.83, 309.67, 960.3100000000001, 823.1200000000001, 0.90592], [570.91, 449.65, 630.539, 630.54, 0.62052], [549.75, 455.43, 570.185, 518.736, 0.56116], [1149.9, 429.71, 1191.7710000000002, 557.3199999999999, 0.34994]], "detections_before": [[566.69, 453.55, 622.259, 622.26, 0.551], [1149.9, 429.71, 1191.7710000000002, 557.3199999999999, 0.86818]], "detections_before_before": [[571.03, 454.91, 622.81, 612.25, 0.53513]], "average_area": [50520.77966317534], "iou_threshold": 0.3, "iteration": 66, "return_matches": [[1, 0, 0]], "return_del_ind": [[1, 0, 0]]},
+            {"detections": [], "detections_before": [[566.69, 453.55, 622.259, 622.26, 0.551], [1149.9, 429.71, 1191.7710000000002, 557.3199999999999, 0.86818]], "detections_before_before": [[571.03, 454.91, 622.81, 612.25, 0.53513]], "average_area": [50520.77966317534], "iou_threshold": 0.3, "iteration": 66, "return_matches": [], "return_del_ind": None},
+            {"detections": [[789.83, 309.67, 960.3100000000001, 823.1200000000001, 0.90592], [570.91, 449.65, 630.539, 630.54, 0.62052], [549.75, 455.43, 570.185, 518.736, 0.56116], [1149.9, 429.71, 1191.7710000000002, 557.3199999999999, 0.34994]], "detections_before": [], "detections_before_before": [[571.03, 454.91, 622.81, 612.25, 0.53513]], "average_area": [50520.77966317534], "iou_threshold": 0.3, "iteration": 66, "return_matches": [], "return_del_ind": None},
+            {"detections": [[789.83, 309.67, 960.3100000000001, 823.1200000000001, 0.90592], [570.91, 449.65, 630.539, 630.54, 0.62052], [549.75, 455.43, 570.185, 518.736, 0.56116], [1149.9, 429.71, 1191.7710000000002, 557.3199999999999, 0.34994]], "detections_before": [[566.69, 453.55, 622.259, 622.26, 0.551], [1149.9, 429.71, 1191.7710000000002, 557.3199999999999, 0.86818]], "detections_before_before": [], "average_area": [50520.77966317534], "iou_threshold": 0.3, "iteration": 66, "return_matches": [], "return_del_ind": None},
+        ]
+
+        for t in test_data:
+            detections = list(map(lambda x: np.array(x), t['detections']))
+            detections_before = list(map(lambda x: np.array(x), t['detections_before']))
+            detections_before_before = list(map(lambda x: np.array(x), t['detections_before_before']))
+            average_area = list(map(lambda x: np.array(x), t['average_area']))
+            ret = association.find_new_trackers_2(detections, detections_before, detections_before_before,
+                                                               average_area, t['iou_threshold'])
+            if len(detections) > 0 and len(detections_before) > 0 and len(detections_before_before) > 0:
+                self.assertListEqual(t['return_matches'], ret[0].tolist())
+                self.assertListEqual(t['return_del_ind'], ret[1].tolist())
+            else:
+                self.assertListEqual(t['return_matches'], ret.tolist())
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/spec/libs/test_calculate_cost.py b/spec/libs/test_calculate_cost.py
new file mode 100644
index 0000000..49f6a2f
--- /dev/null
+++ b/spec/libs/test_calculate_cost.py
@@ -0,0 +1,94 @@
+import unittest
+
+import numpy as np
+
+from libs import calculate_cost
+
+
+class TestCalculateCost(unittest.TestCase):
+    def test_iou(self):
+        self.assertEqual(calculate_cost.iou([0, 0, 1, 1], [0, 0, 1, 1]), 1)
+        self.assertEqual(calculate_cost.iou([1, 1, 2, 3], [1, 1, 1.1, 1.2]), 0.010000000000000007)
+
+    test_data_matrix = [
+        {
+            'detections': [
+                [1, 1, 2, 3],
+                [1, 2, 3, 4],
+                [0, 0, 0.5, 0.2]
+            ],
+            'trackers': [
+                [1, 1, 1.1, 1.2],
+                [1, 3, 2, 5]
+            ],
+            'test': [
+                {
+                    'function': calculate_cost.cal_iou,
+                    'expected': np.array([[0.009999999776482582, 0.0], [0.0, 0.20000000298023224], [0.0, 0.0]],
+                                         np.float32)
+                },
+                {
+                    'function': calculate_cost.cal_area_cost,
+                    'expected': np.array([[99.0, 0.0], [199.0, 1.0], [4.0, 19.0]])
+                },
+            ]
+        },
+        {
+            'detections': [
+                [1, 1, 2, 3],
+                [1, 2, 3, 4],
+                [0, 0, 0.5, 0.2]
+            ],
+            'trackers': [],
+            'test': [
+                {
+                    'function': calculate_cost.cal_iou,
+                    'expected': np.array([[], [], []], np.float32)
+                },
+                {
+                    'function': calculate_cost.cal_area_cost,
+                    'expected': np.array([[], [], []], np.float32)
+                }
+            ]
+        },
+        {
+            'detections': [
+                [1, 1, 2, 3],
+                [1, 2, 3, 4],
+                [0, 0, 0.5, 0.2]
+            ],
+            'trackers': [1, 2, 3, 4],
+            'test': [
+                {
+                    'function': calculate_cost.cal_ios,
+                    'expected': np.array([[0.25], [1.0], [0.0]], np.float32)
+                }
+            ]
+        }
+    ]
+
+    def test_cal_iou_area_cost_ios(self):
+        for test_data in self.test_data_matrix:
+            detections = test_data['detections']
+            trackers = test_data['trackers']
+            for test in test_data['test']:
+                expected = test['expected']
+                actual = test['function'](detections, trackers)
+                self.assertListEqual(expected.tolist(), actual.tolist())
+
+    def test_cal_outside(self):
+        test_data_matrix = [
+            {
+                'trackers': [[-1, -200, 300, 400]],
+                'img_s': [50, 50],
+                'expected': np.array([[1.4138981]], np.float32)
+            }
+        ]
+
+        for test in test_data_matrix:
+            actual = calculate_cost.cal_outside(test['trackers'], test['img_s'])
+            self.assertEqual(test['expected'], actual)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/spec/libs/test_convert.py b/spec/libs/test_convert.py
new file mode 100644
index 0000000..910a409
--- /dev/null
+++ b/spec/libs/test_convert.py
@@ -0,0 +1,16 @@
+import unittest
+from libs import convert
+
+
+class TestConvert(unittest.TestCase):
+    def test_bbox_to_z(self):
+        self.assertListEqual(convert.bbox_to_z([1, 2, 3, 4]).tolist(), [[2.0], [3.0], [4.0], [1.0]])
+
+    def test_x_to_bbox(self):
+        self.assertListEqual(convert.x_to_bbox([1, 2, 3, 4]).tolist(), [[-0.7320508075688772, 1.5669872981077806, 2.732050807568877, 2.433012701892219]])
+        self.assertListEqual(convert.x_to_bbox([1, 2, 3, 4], 0.5).tolist(),
+                             [[-0.7320508075688772, 1.5669872981077806, 2.732050807568877, 2.433012701892219, 0.5]])
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/spec/libs/test_kalman_tracker.py b/spec/libs/test_kalman_tracker.py
new file mode 100644
index 0000000..ed971c0
--- /dev/null
+++ b/spec/libs/test_kalman_tracker.py
@@ -0,0 +1,79 @@
+import unittest
+
+from libs.kalman_tracker import KalmanBoxTracker
+
+
+class TestKalmanTracker(unittest.TestCase):
+    bbox = [[1., 2., 3., 4.], [1.1, 2.1, 3.1, 4.1], [1.2, 2.2, 3.2, 4.2]]
+
+    def test_ctor_increment_id(self):
+        filter1 = KalmanBoxTracker(self.bbox[0], 0, self.bbox[0])
+        filter2 = KalmanBoxTracker(self.bbox[0], 0, self.bbox[0])
+        self.assertEqual(filter1.id + 1, filter2.id)
+
+    def test_ctor_0(self):
+        filter1 = KalmanBoxTracker(self.bbox[0], 0, self.bbox[0])
+        self.assertListEqual(filter1.kf.x.tolist(), [[2.], [3.], [4.], [1.], [0.], [0.], [0.]])
+        self.assertListEqual(filter1.get_state().tolist(), [self.bbox[0]])
+
+    def test_ctor_1(self):
+        filter1 = KalmanBoxTracker(self.bbox[0], 1, [1, 2.1, 3.2, 4.3])
+        self.assertListEqual(filter1.kf.x.tolist(),
+                             [[2.], [3.], [4.], [1.], [-0.10000000000000009], [-0.20000000000000018],
+                              [-0.8399999999999999]])
+        self.assertListEqual(filter1.get_state().tolist(), [self.bbox[0]])
+
+    def test_0_predict(self):
+        f = KalmanBoxTracker(self.bbox[0], 0, self.bbox[0])
+        actual = f.predict()
+        self.assertListEqual([self.bbox[0]], actual.tolist())
+        actual = f.predict()
+        self.assertListEqual([self.bbox[0]], actual.tolist())
+
+    def test_1_predict(self):
+        f = KalmanBoxTracker(self.bbox[0], 1, self.bbox[0])
+        actual = f.predict()
+        self.assertListEqual([self.bbox[0]], actual.tolist())
+        actual = f.predict()
+        self.assertListEqual([self.bbox[0]], actual.tolist())
+
+    def test_0_update_0(self):
+        f = KalmanBoxTracker(self.bbox[0], 0, self.bbox[0])
+        f.update(self.bbox[0], 0)
+        actual = f.predict()
+        self.assertListEqual([self.bbox[0]], actual.tolist())
+
+    def test_0_update_0_multiple(self):
+        f = KalmanBoxTracker(self.bbox[0], 0, self.bbox[0])
+        for x in self.bbox:
+            f.update(x, 0)
+        actual = f.predict()
+        self.assertListEqual([self.bbox[0]], actual.tolist())
+
+    def test_0_update_1_multiple(self):
+        f = KalmanBoxTracker(self.bbox[0], 0, self.bbox[0])
+        for x in self.bbox:
+            f.update(x, 1)
+        actual = f.predict()
+        expected = [[1.096774193548387, 2.096774193548387, 3.096774193548387, 4.096774193548387]]
+        self.assertListEqual(expected, actual.tolist())
+        self.assertListEqual(expected, f.get_state().tolist())
+
+    def test_1_update_1_negative(self):
+        f = KalmanBoxTracker(self.bbox[0], 1, [1, 2, 3.5, 4.5])
+        for x in [self.bbox[0], [1, 2, 1.5, 3.5], [1, 2, 2, 3], [1, 2, 1.1, 2.1]]:
+            f.predict()
+            f.update(x, 1)
+        actual = f.predict()
+        expected = [[0.7705099024374483, 1.8092846803759595, 0.9155530672670072, 1.9636854687429093]]
+        self.assertListEqual(expected, actual.tolist())
+
+    def test_0_update_1(self):
+        f = KalmanBoxTracker(self.bbox[0], 0, self.bbox[0])
+        f.update(self.bbox[0], 1)
+        actual = f.predict()
+        self.assertListEqual([self.bbox[0]], actual.tolist())
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/spec/libs/test_tracker.py b/spec/libs/test_tracker.py
new file mode 100644
index 0000000..4b5693c
--- /dev/null
+++ b/spec/libs/test_tracker.py
@@ -0,0 +1,347 @@
+import json
+import unittest
+
+import numpy as np
+
+from libs.convert import x_to_bbox
+from libs.kalman_tracker import KalmanBoxTracker
+from libs.tracker import Sort_OH, _remove_outside_trackers, _build_new_targets, _remove_dead_tracklet, \
+    _init_area_and_trackers, _update_matched_trackers
+from libs.utils import DictObj, list_of_ndarray_tolist
+
+
+def kalman_box_tracker_to_json_no_state(t):
+    tjson = t
+    if isinstance(t, KalmanBoxTracker):
+        tjson = t.to_json()
+
+    tjson.pop('get_state')
+    tjson.pop('time_since_update')
+    tjson.pop('id')
+    tjson.pop('kf')
+    return tjson
+
+
+def kalman_tracker_mock_from_json(t):
+    t_obj = DictObj(t)
+    t_obj.update = lambda x, y: None
+
+    if 'get_state' in t.keys():
+        t_obj.get_state = lambda: t['get_state']
+    if 'kf' in t.keys():
+        t_obj.predict = lambda: x_to_bbox(t['kf']['x'])
+
+    return t_obj
+
+class TestTracker(unittest.TestCase):
+    def assertNdArrayEqual(self, actual, expected, index=None):
+        if index is None:
+            index_copy = []
+        else:
+            index_copy = index.copy()
+
+        self.assertEqual(len(actual), len(expected), msg="Length at index %s" % index_copy)
+        for i in range(len(expected)):
+            index_copy.append(i)
+
+            if isinstance(expected[i], list):
+                self.assertNdArrayEqual(actual[i], expected[i], index=index_copy)
+            else:
+                self.assertAlmostEqual(actual[i], expected[i], msg="Value at index %s" % index_copy)
+
+    def assertSortOHJsonDictEqual(self, actual, expected):
+        self.assertEqual(actual['max_age'], expected['max_age'])
+        self.assertEqual(actual['min_hits'], expected['min_hits'])
+        self.assertListEqual(
+            list(map(lambda t: kalman_box_tracker_to_json_no_state(t), actual['trackers'])),
+            expected['trackers'])
+        self.assertEqual(len(actual['area_avg_array']), len(expected['area_avg_array']))
+        for i in range(len(actual['area_avg_array'])):
+            self.assertAlmostEqual(actual['area_avg_array'][i], expected['area_avg_array'][i], msg="index: %i" % i)
+        self.assertEqual(actual['frame_count'], expected['frame_count'])
+        self.assertNdArrayEqual(actual['unmatched_before_before'], expected['unmatched_before_before'])
+        self.assertNdArrayEqual(actual['unmatched_before'], expected['unmatched_before'])
+        self.assertNdArrayEqual(actual['unmatched'], expected['unmatched'])
+        self.assertNdArrayEqual(actual['scene'], expected['scene'])
+        self.assertEqual(actual['conf_trgt'], expected['conf_trgt'])
+        self.assertEqual(actual['conf_objt'], expected['conf_objt'])
+
+    def test_to_json_empty(self):
+        self.assertSortOHJsonDictEqual(Sort_OH().to_json(), {
+            "max_age": 3,
+            "min_hits": 3,
+            "trackers": [],
+            "area_avg_array": [],
+            "frame_count": 0,
+            "unmatched_before_before": [],
+            "unmatched_before": [],
+            "unmatched": [],
+            "scene": [1920, 1080],
+            "conf_trgt": 0,
+            "conf_objt": 0
+        })
+        self.assertSortOHJsonDictEqual(Sort_OH(1, 2).to_json(), {
+            "max_age": 1,
+            "min_hits": 2,
+            "trackers": [],
+            "area_avg_array": [],
+            "frame_count": 0,
+            "unmatched_before_before": [],
+            "unmatched_before": [],
+            "unmatched": [],
+            "scene": [1920, 1080],
+            "conf_trgt": 0,
+            "conf_objt": 0
+        })
+
+    def test_to_json_filled(self):
+        detections = [[1359.1, 413.27, 1479.36, 776.04, 2.4731], [584.04, 446.86, 668.7819999999999, 703.09, 1.2369],
+                      [729.0, 457.0, 768.0, 576.0, 0.40858]]
+        groundtruths = [[912.0, 484.0, 1009.0, 593.0, 0.0], [1342.0, 417.0, 1510.0, 797.0, 1.0],
+                        [586.0, 446.0, 671.0, 710.0, 1.0], [1585.0, -1.0, 1921.0, 577.0, 0.0],
+                        [1163.0, 441.0, 1196.0, 530.0, 0.0], [1308.0, 431.0, 1342.0, 549.0, 0.0],
+                        [1422.0, 431.0, 1605.0, 768.0, 1.0], [1055.0, 483.0, 1091.0, 593.0, 1.0],
+                        [1090.0, 484.0, 1122.0, 598.0, 1.0], [733.0, 487.0, 763.0, 555.0, 0.0],
+                        [679.0, 492.0, 731.0, 597.0, 0.0], [737.0, 457.0, 764.0, 532.0, 0.0],
+                        [1255.0, 447.0, 1288.0, 547.0, 1.0], [1015.0, 430.0, 1055.0, 546.0, 1.0],
+                        [1100.0, 440.0, 1138.0, 548.0, 1.0], [934.0, 435.0, 976.0, 549.0, 1.0],
+                        [442.0, 446.0, 549.0, 728.0, 1.0], [636.0, 458.0, 697.0, 645.0, 1.0],
+                        [1365.0, 434.0, 1417.0, 558.0, 1.0], [1480.0, 433.0, 1542.0, 558.0, 1.0],
+                        [473.0, 460.0, 562.0, 709.0, 1.0], [835.0, 473.0, 887.0, 548.0, 0.0],
+                        [796.0, 476.0, 851.0, 536.0, 0.0], [547.0, 464.0, 582.0, 557.0, 1.0],
+                        [375.0, 446.0, 416.0, 550.0, 0.0], [418.0, 459.0, 458.0, 543.0, 1.0],
+                        [582.0, 455.0, 616.0, 589.0, 1.0], [972.0, 456.0, 1004.0, 533.0, 1.0],
+                        [693.0, 462.0, 714.0, 529.0, 0.0], [712.0, 477.0, 732.0, 534.0, 0.0],
+                        [733.0, 504.0, 764.0, 549.0, 0.0], [910.0, 408.0, 936.0, 537.0, 0.0],
+                        [730.0, 509.0, 767.0, 569.0, 0.0], [679.0, 528.0, 725.0, 607.0, 0.0],
+                        [1003.0, 453.0, 1021.0, 514.0, 0.0], [578.0, 431.0, 598.0, 474.0, 1.0],
+                        [595.0, 428.0, 613.0, 470.0, 1.0], [1035.0, 452.0, 1060.0, 519.0, 1.0],
+                        [664.0, 451.0, 698.0, 536.0, 1.0]]
+
+        t = Sort_OH()
+        for i in range(5):
+            t.update(np.array(detections), np.array(groundtruths))
+
+        self.assertSortOHJsonDictEqual(t.to_json(), {
+            "max_age": 3,
+            "min_hits": 3,
+            "trackers": [
+                {'age': 4, 'confidence': 0.5, 'time_since_observed': 0},
+                {'age': 4, 'confidence': 0.5, 'time_since_observed': 0}
+            ],
+            "area_avg_array": [0, 32670.08143, 32670.08143, 32670.08143, 32670.08143],
+            "frame_count": 5,
+            "unmatched_before_before": [[7.2900e+02, 4.5700e+02, 7.6800e+02, 5.7600e+02, 4.0858e-01]],
+            "unmatched_before": [[7.2900e+02, 4.5700e+02, 7.6800e+02, 5.7600e+02, 4.0858e-01]],
+            "unmatched": [[7.2900e+02, 4.5700e+02, 7.6800e+02, 5.7600e+02, 4.0858e-01]],
+            "scene": [1920, 1080],
+            "conf_trgt": 0,
+            "conf_objt": 0
+        })
+
+    def test_update_invocations(self):
+        with open('spec/res/tracker_update.json') as f:
+            invocations = json.load(f)['invocations']
+
+            # Reset Kalman Tracker ID which got incremented in previous tests
+            KalmanBoxTracker.count = 0
+
+            # Make sure tracker is built as in the invocations
+            t = Sort_OH(invocations[0]['self']['max_age'], invocations[0]['self']['min_hits'], np.array([1920, 1080]))
+            t.conf_trgt = invocations[0]['self']['conf_trgt']
+            t.conf_objt = invocations[0]['self']['conf_objt']
+
+            for x in invocations:
+                in_dets = np.array(x['dets'])
+                in_gts = np.array(x['gts'])
+                trackers, unmatched_trks_pos, unmatched_gts_pos = t.update(in_dets, in_gts)
+
+                # Check main outputs
+                self.assertNdArrayEqual(trackers, x['ret_ret'])
+                self.assertNdArrayEqual(unmatched_trks_pos, x['ret_unmatched_trks_pos'])
+                self.assertNdArrayEqual(unmatched_gts_pos, x['ret_unmatched_gts_pos'])
+
+                # Check for side effects on inputs
+                self.assertNdArrayEqual(in_dets, x['ret_dets'])
+                self.assertNdArrayEqual(in_gts, x['ret_gts'])
+                x['ret_self']['scene'] = [1920, 1080]  # HACK: Supply this info after refactoring manually
+                self.assertSortOHJsonDictEqual(t.to_json(), x['ret_self'])
+
+    def test__init_area_and_trackers(self):
+        x = {
+            "kalman_trackers": [
+                {"time_since_observed": 0, "confidence": 0.5, "age": 100, "get_state": [[652.5021638258046, 429.89314515922297, 752.8235272240348, 732.8593911610949]], "time_since_update": 1, "id": 1, "kf": {"x": [702.6628455249197, 581.3762681601589, 30393.986862551403, 0.3311304962916904, 1.8372252619403222, -0.015659604650060353, 48.40383331102944]}},
+                {"time_since_observed": 0, "confidence": 1, "age": 80, "get_state": [[507.46718038184713, 444.4740225602785, 612.0308138979817, 760.1653184821718]], "time_since_update": 1, "id": 4, "kf": {"x": [559.7489971399144, np.nan, 33009.82897101042, 0.33122114821311116, -0.13915475487793044, 0.08126716203647405, 7.688792975854]}},
+                {"time_since_observed": 0, "confidence": 0.5, "age": 18, "get_state": [[759.1778340001522, 447.9602113881072, 843.5349907598177, 703.0550763755507]], "time_since_update": 1, "id": 6, "kf": {"x": [801.3564123799849, 575.5076438818289, 21519.077514331446, 0.33068935654121345, 2.916506192292385, 0.6870007981125773, 212.2750733010161]}},
+                {"time_since_observed": 0, "confidence": 0.4204631042226629, "age": 16, "get_state": [[559.8197969819889, 437.24035029259085, 678.86542161119, 796.3669335084915]], "time_since_update": 1, "id": 7, "kf": {"x": [619.3426092965894, 616.8036419005412, 42752.44841988766, 0.33148652924318034, -0.06572298440042505, 0.32076230568408565, -52.95193989110717]}}
+            ],
+            "ret_kalman_trackers": [
+                {"time_since_observed": 0, "confidence": 0.5, "age": 100, "get_state": [[652.5021638258046, 429.89314515922297, 752.8235272240348, 732.8593911610949]], "time_since_update": 1, "id": 1, "kf": {"x": [702.6628455249197, 581.3762681601589, 30393.986862551403, 0.3311304962916904, 1.8372252619403222, -0.015659604650060353, 48.40383331102944]}},
+                {"time_since_observed": 0, "confidence": 0.5, "age": 18, "get_state": [[759.1778340001522, 447.9602113881072, 843.5349907598177, 703.0550763755507]], "time_since_update": 1, "id": 6, "kf": {"x": [801.3564123799849, 575.5076438818289, 21519.077514331446, 0.33068935654121345, 2.916506192292385, 0.6870007981125773, 212.2750733010161]}},
+                {"time_since_observed": 0, "confidence": 0.4204631042226629, "age": 16, "get_state": [[559.8197969819889, 437.24035029259085, 678.86542161119, 796.3669335084915]], "time_since_update": 1, "id": 7, "kf": {"x": [619.3426092965894, 616.8036419005412, 42752.44841988766, 0.33148652924318034, -0.06572298440042505, 0.32076230568408565, -52.95193989110717]}}
+            ],
+            "ret_trks": [
+                [652.5021638258046, 429.89314515922297, 752.8235272240348, 732.8593911610949, 0.0],
+                [759.1778340001522, 447.9602113881072, 843.5349907598177, 703.0550763755507, 0.0],
+                [559.8197969819889, 437.24035029259085, 678.86542161119, 796.3669335084915, 0.0]
+            ],
+            "ret_area_avg": 31918.83544194523
+        }
+        kalman_trackers = list(map(lambda t: kalman_tracker_mock_from_json(t), x['kalman_trackers']))
+        trks, area_avg = _init_area_and_trackers(kalman_trackers)
+        self.assertListEqual(trks.tolist(), x["ret_trks"])
+        self.assertEqual(area_avg, x["ret_area_avg"])
+        self.maxDiff = None
+        self.assertEqual(len(kalman_trackers), len(x['ret_kalman_trackers']))
+        self.assertListEqual(
+            list(map(lambda t: kalman_tracker_mock_from_json(t) if isinstance(t, KalmanBoxTracker) else t, kalman_trackers)),
+            list(map(lambda t: kalman_tracker_mock_from_json(t), x['ret_kalman_trackers'])))
+
+    def test__remove_outside_trackers(self):
+        x = {"trks": [[1359.1, 1413.27, 1479.3600000000001, 1776.04, 0.0],
+                      [584.04, 446.86, 668.7819999999999, 703.09, 0.0]], "scene": [1920, 1080],
+             "kalman_trackers": [{"time_since_observed": 0, "confidence": 0.5, "age": 1},
+                                 {"time_since_observed": 0, "confidence": 0.5, "age": 1}],
+             "ret_kalman_trackers": [{"time_since_observed": 0, "confidence": 0.5, "age": 1}],
+             "ret_trks": [[584.04, 446.86, 668.7819999999999, 703.09, 0.0]]}
+        kalman_trackers = list(map(lambda t: DictObj(t), x['kalman_trackers']))
+        actual = _remove_outside_trackers(x['trks'], kalman_trackers, np.array(x['scene']))
+        self.assertListEqual(actual.tolist(), x['ret_trks'])
+        self.assertListEqual(kalman_trackers, list(map(lambda t: DictObj(t), x['ret_kalman_trackers'])))
+
+    def test__update_matched_trackers(self):
+        def testAndExpect(x):
+            kalman_trackers = list(map(lambda t: kalman_tracker_mock_from_json(t), x['kalman_trackers']))
+            trks = np.array(x['trks'])
+            unmatched_trks_pos = _update_matched_trackers(np.array(x['dets']), kalman_trackers, x['unmatched_trks'],
+                                                          x['occluded_trks'], np.array(x['matched']), trks)
+            self.assertListEqual(
+                list(map(lambda x: x.tolist(), unmatched_trks_pos[0])),
+                x['ret_unmatched_trks_pos']
+            )
+            self.assertListEqual(
+                list(map(lambda x: x.tolist(), trks)),
+                x['ret_trks']
+            )
+            self.assertListEqual(
+                list(map(lambda t: kalman_tracker_mock_from_json(t) if isinstance(t, KalmanBoxTracker) else t,
+                         kalman_trackers)),
+                list(map(lambda t: kalman_tracker_mock_from_json(t), x['ret_kalman_trackers'])))
+
+        testAndExpect({
+            "dets": [
+                [1434.1, 389.14, 1582.3899999999999, 836.0, 2.3735],
+                [589.13, 442.1, 680.026, 716.79, 1.3351],
+                [1528.8, 413.27, 1649.06, 776.04, 1.3038],
+                [729.0, 449.0, 768.0, 568.0, 0.55511],
+                [1254.6, 446.72, 1288.422, 550.19, 0.39739],
+                [478.71, 493.64, 552.353, 716.5699999999999, 0.33771]
+            ],
+            "kalman_trackers": [{"time_since_observed": 0, "confidence": 0.5, "age": 12, "get_state": [[1455.7167590735019, 389.9308521095137, 1593.3395129548937, 804.8326703780252]], "time_since_update": 1, "id": 0, "kf": {"x": [[1524.5281360141978], [597.3817612437695], [57099.930820509384], [0.33169956799834216], [9.641498354998095], [0.2512390527014783], [1261.1879477256625]]}}, {"time_since_observed": 0, "confidence": 0.5, "age": 12, "get_state": [[586.950585237224, 446.0025872289186, 674.1783178789447, 709.6749115280907]], "time_since_update": 1, "id": 1, "kf": {"x": [[630.5644515580843], [577.8387493785046], [22999.53900898925], [0.33081868896770894], [0.0892406806373982], [0.5203642914075408], [-303.28790311845853]]}}, {"time_since_observed": 0, "confidence": 0.5, "age": 7, "get_state": [[1536.9790160857324, 413.27, 1657.2390160857326, 776.04]], "time_since_update": 1, "id": 2, "kf": {"x": [[1597.1090160857325], [594.655], [43626.720199999996], [0.3315048102103261], [7.289484157179798], [0.0], [0.0]]}}],
+            "unmatched_trks": [1],
+            "occluded_trks": [2],
+            "matched": [[0, 0], [1, 1], [2, 2]],
+            "trks": [[1455.7167590735019, 389.9308521095137, 1593.3395129548937, 804.8326703780252, 0.0], [586.950585237224, 446.0025872289186, 674.1783178789447, 709.6749115280907, 0.0], [1536.9790160857324, 413.27, 1657.2390160857326, 776.04, 0.0]],
+            "ret_kalman_trackers": [{"time_since_observed": 0, "confidence": 0.5, "age": 12, "get_state": [[1455.7167590735019, 389.9308521095137, 1593.3395129548937, 804.8326703780252]], "time_since_update": 1, "id": 0, "kf": {"x": [[1524.5281360141978], [597.3817612437695], [57099.930820509384], [0.33169956799834216], [9.641498354998095], [0.2512390527014783], [1261.1879477256625]]}}, {"time_since_observed": 0, "confidence": 0.5, "age": 12, "get_state": [[586.950585237224, 446.0025872289186, 674.1783178789447, 709.6749115280907]], "time_since_update": 1, "id": 1, "kf": {"x": [[630.5644515580843], [577.8387493785046], [22999.53900898925], [0.33081868896770894], [0.0892406806373982], [0.5203642914075408], [-303.28790311845853]]}}, {"time_since_observed": 0, "confidence": 0.5, "age": 7, "get_state": [[1536.9790160857324, 413.27, 1657.2390160857326, 776.04]], "time_since_update": 1, "id": 2, "kf": {"x": [[1597.1090160857325], [594.655], [43626.720199999996], [0.3315048102103261], [7.289484157179798], [0.0], [0.0]]}}],
+            "ret_unmatched_trks_pos": [[586.950585237224, 446.0025872289186, 674.1783178789447, 709.6749115280907, 2.0]],
+            "ret_trks": [
+                [1455.7167590735019, 389.9308521095137, 1593.3395129548937, 804.8326703780252, 0.0],
+                [586.950585237224, 446.0025872289186, 674.1783178789447, 709.6749115280907, 0.0],
+                [1536.9790160857324, 413.27, 1657.2390160857326, 776.04, 0.0]
+            ]})
+
+    def test__build_new_targets(self):
+        def testAndExpect(x):
+            kalman_trackers = list(map(lambda t: DictObj(t), x['kalman_trackers']))
+            _build_new_targets(
+                x['unmatched_before_before'],
+                x['unmatched_before'],
+                x['unmatched'],
+                x['area_avg'],
+                kalman_trackers
+            )
+            self.assertListEqual(x['unmatched_before_before'], x['ret_unmatched_before_before'])
+            self.assertListEqual(x['unmatched_before'], x['ret_unmatched_before'])
+            self.assertListEqual(x['unmatched'], x['ret_unmatched'])
+            self.assertEqual(x['area_avg'], x['ret_area_avg'])
+
+            self.assertListEqual(
+                list(map(lambda t: DictObj(kalman_box_tracker_to_json_no_state(t)) if isinstance(t,
+                                                                                                 KalmanBoxTracker) else t,
+                         kalman_trackers)),
+                list(map(lambda t: DictObj(t), x['ret_kalman_trackers'])))
+
+        testAndExpect({"unmatched_before_before": [[1.0, 389.14, 149.29, 836.0, 0.6479],
+                                                   [1198.9, 446.72, 1232.7220000000002, 550.19, 0.5088]],
+                       "unmatched_before": [[30.857, 389.14, 179.147, 836.0, 0.78032]],
+                       "unmatched": [[30.857, 389.14, 179.147, 836.0, 1.2889]], "area_avg": 81108.71979844959,
+                       "kalman_trackers": [{"time_since_observed": 7, "confidence": 1, "age": 314},
+                                           {"time_since_observed": 0, "confidence": 0.3447157409799234, "age": 232},
+                                           {"time_since_observed": 0, "confidence": 1, "age": 230},
+                                           {"time_since_observed": 8, "confidence": 1, "age": 159},
+                                           {"time_since_observed": 0, "confidence": 0.5, "age": 121},
+                                           {"time_since_observed": 1, "confidence": 0.6744409583467609, "age": 106},
+                                           {"time_since_observed": 0, "confidence": 0.5, "age": 95}],
+                       "ret_unmatched_before_before": [[1198.9, 446.72, 1232.7220000000002, 550.19, 0.5088]],
+                       "ret_unmatched_before": [], "ret_unmatched": [], "ret_area_avg": 81108.71979844959,
+                       "ret_kalman_trackers": [{"time_since_observed": 7, "confidence": 1, "age": 314},
+                                               {"time_since_observed": 0, "confidence": 0.3447157409799234, "age": 232},
+                                               {"time_since_observed": 0, "confidence": 1, "age": 230},
+                                               {"time_since_observed": 8, "confidence": 1, "age": 159},
+                                               {"time_since_observed": 0, "confidence": 0.5, "age": 121},
+                                               {"time_since_observed": 1, "confidence": 0.6744409583467609, "age": 106},
+                                               {"time_since_observed": 0, "confidence": 0.5, "age": 95},
+                                               {"time_since_observed": 0, "confidence": 0.5, "age": 0}]});
+        testAndExpect({"unmatched_before_before": [[1254.6, 446.72, 1288.422, 550.19, 0.5207],
+                                                   [729.81, 446.86, 771.6809999999999, 574.47, 0.49008]],
+                       "unmatched_before": [[1254.6, 446.72, 1288.422, 550.19, 0.9907],
+                                            [729.81, 446.86, 771.6809999999999, 574.47, 0.52778],
+                                            [481.15, 464.01, 565.8919999999999, 720.24, 0.45687]],
+                       "unmatched": [[1254.6, 446.72, 1288.422, 550.19, 0.89436],
+                                     [478.86, 460.48, 569.756, 735.1700000000001, 0.48132],
+                                     [729.81, 446.86, 771.6809999999999, 574.47, 0.33441]],
+                       "area_avg": 46961.41488824861,
+                       "kalman_trackers": [{"time_since_observed": 0, "confidence": 0.5, "age": 15},
+                                           {"time_since_observed": 0, "confidence": 0.5, "age": 15},
+                                           {"time_since_observed": 0, "confidence": 0.5, "age": 10}],
+                       "ret_unmatched_before_before": [[1254.6, 446.72, 1288.422, 550.19, 0.5207]],
+                       "ret_unmatched_before": [[1254.6, 446.72, 1288.422, 550.19, 0.9907],
+                                                [481.15, 464.01, 565.8919999999999, 720.24, 0.45687]],
+                       "ret_unmatched": [[1254.6, 446.72, 1288.422, 550.19, 0.89436],
+                                         [478.86, 460.48, 569.756, 735.1700000000001, 0.48132]],
+                       "ret_area_avg": 46961.41488824861,
+                       "ret_kalman_trackers": [{"time_since_observed": 0, "confidence": 0.5, "age": 15},
+                                               {"time_since_observed": 0, "confidence": 0.5, "age": 15},
+                                               {"time_since_observed": 0, "confidence": 0.5, "age": 10},
+                                               {"time_since_observed": 0, "confidence": 0.5, "age": 0}]});
+
+        with open('spec/res/tracker__build_new_targets.json') as f:
+            invocations = json.load(f)['invocations']
+            for x in invocations:
+                testAndExpect(x)
+
+    def test__remove_dead_tracklet(self):
+        x = {
+                "kalman_trackers": [
+                    {"time_since_observed": 0, "confidence": 0.5, "age": 55, "get_state": [[618.7167233320538, 446.82986314705045, 703.5705941709728, 703.3933628232828]], "time_since_update": 0, "id": 1},
+                    {"time_since_observed": 6, "confidence": 0.12095521555989777, "age": 40, "get_state": [[1264.2954991567915, 446.5118540805074, 1298.3723086666594, 550.7502364662395]], "time_since_update": 6, "id": 3},
+                    {"time_since_observed": 0, "confidence": 0.5, "age": 35, "get_state": [[486.52492777282396, 444.2223193633019, 591.2660093965626, 760.4490279480096]], "time_since_update": 100, "id": 4}
+                ],
+                "max_age": 3,
+                "ret_kalman_trackers": [
+                    {"time_since_observed": 0, "confidence": 0.5, "age": 55, "get_state": [[618.7167233320538, 446.82986314705045, 703.5705941709728, 703.3933628232828]], "time_since_update": 0, "id": 1},
+                    {"time_since_observed": 6, "confidence": 0.12095521555989777, "age": 40, "get_state": [[1264.2954991567915, 446.5118540805074, 1298.3723086666594, 550.7502364662395]], "time_since_update": 6, "id": 3},
+                ],
+                "ret": [[[618.7167233320538, 446.82986314705045, 703.5705941709728, 703.3933628232828, 2.0]]]
+        }
+
+        kalman_trackers = list(map(lambda t: kalman_tracker_mock_from_json(t), x['kalman_trackers']))
+        ret = _remove_dead_tracklet(kalman_trackers, x['max_age'])
+        self.assertListEqual(list_of_ndarray_tolist(ret), x['ret'])
+        self.assertListEqual(
+            list(map(lambda t: kalman_tracker_mock_from_json(t) if isinstance(t, KalmanBoxTracker) else t, kalman_trackers)),
+            list(map(lambda t: kalman_tracker_mock_from_json(t), x['ret_kalman_trackers'])))
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/spec/res/associate__clean_up_to_delete.json b/spec/res/associate__clean_up_to_delete.json
new file mode 100644
index 0000000..87fb206
--- /dev/null
+++ b/spec/res/associate__clean_up_to_delete.json
@@ -0,0 +1,162 @@
+[{"matches_ext_com": [[[0, 0, 0]]], "unmatched_detections": [2], "unmatched_trackers": [1], "matches": [[0, 6], [1, 4], [3, 5], [4, 2], [5, 3], [6, 0]], "unmatched_before": [[1373.3, 402.13, 1477.86, 717.81, 0.86749]], "ret_unmatched_before": [], "ret_matches": [[0, 6], [1, 4], [3, 5], [4, 2], [5, 3], [6, 0], [2, 1]], "ret_unmatched_trackers": [], "ret_unmatched_detections": []},
+{"matches_ext_com": [[[0, 1, 0]]], "unmatched_detections": [3, 5], "unmatched_trackers": [3], "matches": [[0, 4], [1, 5], [2, 2], [4, 6], [6, 0], [7, 1]], "unmatched_before": [[909.86, 429.71, 994.602, 685.94, 0.75014], [713.0, 477.0, 732.0, 536.0, 0.34918]], "ret_unmatched_before": [[713.0, 477.0, 732.0, 536.0, 0.34918]], "ret_matches": [[0, 4], [1, 5], [2, 2], [4, 6], [6, 0], [7, 1], [5, 3]], "ret_unmatched_trackers": [], "ret_unmatched_detections": [3]},
+{"matches_ext_com": [[[0, 3, 0]]], "unmatched_detections": [4, 5, 6, 3], "unmatched_trackers": [1], "matches": [[0, 0], [1, 2], [2, 3]], "unmatched_before": [[938.34, 423.72, 1029.236, 698.4100000000001, 0.41726], [469.67, 451.29, 514.618, 588.13, 0.40819], [505.0, 449.0, 544.0, 568.0, 0.51051]], "ret_unmatched_before": [[469.67, 451.29, 514.618, 588.13, 0.40819], [505.0, 449.0, 544.0, 568.0, 0.51051]], "ret_matches": [[0, 0], [1, 2], [2, 3], [3, 1]], "ret_unmatched_trackers": [], "ret_unmatched_detections": [4, 5, 6]},
+{"matches_ext_com": [[[1, 0, 0]]], "unmatched_detections": [2], "unmatched_trackers": [3, 1], "matches": [[0, 0], [1, 2]], "unmatched_before": [[721.23, 309.67, 891.71, 823.1200000000001, 0.38495]], "ret_unmatched_before": [], "ret_matches": [[0, 0], [1, 2], [2, 1]], "ret_unmatched_trackers": [3], "ret_unmatched_detections": []},
+{"matches_ext_com": [[[1, 0, 0]]], "unmatched_detections": [4], "unmatched_trackers": [3, 2], "matches": [[0, 0], [1, 4], [2, 1], [3, 5]], "unmatched_before": [[846.44, 295.07, 1029.23, 845.45, 0.4188]], "ret_unmatched_before": [], "ret_matches": [[0, 0], [1, 4], [2, 1], [3, 5], [4, 2]], "ret_unmatched_trackers": [3], "ret_unmatched_detections": []},
+{"matches_ext_com": [[[2, 0, 0]]], "unmatched_detections": [1, 4], "unmatched_trackers": [2, 3, 1, 4], "matches": [[0, 5], [2, 6], [3, 0]], "unmatched_before": [[1098.8, 423.24, 1308.92, 1055.6, 0.49083]], "ret_unmatched_before": [], "ret_matches": [[0, 5], [2, 6], [3, 0], [1, 1]], "ret_unmatched_trackers": [2, 3, 4], "ret_unmatched_detections": [4]},
+{"matches_ext_com": [[[2, 0, 0]]], "unmatched_detections": [2, 4], "unmatched_trackers": [0, 2, 3, 1], "matches": [[0, 6], [1, 4], [3, 5]], "unmatched_before": [[1583.4, 419.0, 1731.69, 865.86, 0.35993]], "ret_unmatched_before": [], "ret_matches": [[0, 6], [1, 4], [3, 5], [2, 3]], "ret_unmatched_trackers": [0, 2, 1], "ret_unmatched_detections": [4]},
+{"matches_ext_com": [[[2, 0, 0]]], "unmatched_detections": [3, 4], "unmatched_trackers": [2, 4, 0, 1], "matches": [[0, 5], [1, 3], [2, 6]], "unmatched_before": [[1326.6, 390.88, 1455.56, 779.76, 0.34712]], "ret_unmatched_before": [], "ret_matches": [[0, 5], [1, 3], [2, 6], [3, 0]], "ret_unmatched_trackers": [2, 4, 1], "ret_unmatched_detections": [4]},
+{"matches_ext_com": [[[2, 0, 0]]], "unmatched_detections": [4], "unmatched_trackers": [1, 3, 0], "matches": [[0, 2], [1, 6], [2, 5], [3, 4]], "unmatched_before": [[803.26, 296.57, 1013.38, 928.9300000000001, 0.45407]], "ret_unmatched_before": [], "ret_matches": [[0, 2], [1, 6], [2, 5], [3, 4], [4, 0]], "ret_unmatched_trackers": [1, 3], "ret_unmatched_detections": []},
+{"matches_ext_com": [[[2, 1, 0]]], "unmatched_detections": [2, 4, 5], "unmatched_trackers": [6, 1, 3, 0], "matches": [[0, 2], [1, 5], [3, 4], [6, 7]], "unmatched_before": [[1098.4, 430.92, 1153.969, 599.63, 0.37347]], "ret_unmatched_before": [], "ret_matches": [[0, 2], [1, 5], [3, 4], [6, 7], [4, 3]], "ret_unmatched_trackers": [6, 1, 0], "ret_unmatched_detections": [2, 5]},
+{"matches_ext_com": [[[3, 0, 0]]], "unmatched_detections": [4], "unmatched_trackers": [0, 1, 2, 3], "matches": [[0, 7], [1, 8], [2, 6], [3, 4], [5, 5]], "unmatched_before": [[971.35, 433.93, 1044.993, 656.86, 0.42435], [665.0, 441.0, 704.0, 560.0, 0.33452]], "ret_unmatched_before": [[665.0, 441.0, 704.0, 560.0, 0.33452]], "ret_matches": [[0, 7], [1, 8], [2, 6], [3, 4], [5, 5], [4, 3]], "ret_unmatched_trackers": [0, 1, 2], "ret_unmatched_detections": []},
+{"matches_ext_com": [[[4, 0, 0]]], "unmatched_detections": [5], "unmatched_trackers": [0, 5, 6, 7, 2], "matches": [[0, 4], [1, 3], [2, 1], [3, 9], [4, 8]], "unmatched_before": [[1046.0, 448.86, 1119.643, 671.79, 0.32509]], "ret_unmatched_before": [], "ret_matches": [[0, 4], [1, 3], [2, 1], [3, 9], [4, 8], [5, 2]], "ret_unmatched_trackers": [0, 5, 6, 7], "ret_unmatched_detections": []},
+{"matches_ext_com": [], "unmatched_detections": [1, 3, 4], "unmatched_trackers": [7, 0, 1, 3], "matches": [[0, 2], [2, 5], [5, 4], [6, 6]], "unmatched_before": [[1031.1, 433.93, 1104.743, 656.86, 0.89246], [542.7, 463.91, 566.3230000000001, 536.779, 0.53515]], "ret_unmatched_before": [[1031.1, 433.93, 1104.743, 656.86, 0.89246], [542.7, 463.91, 566.3230000000001, 536.779, 0.53515]], "ret_matches": [[0, 2], [2, 5], [5, 4], [6, 6]], "ret_unmatched_trackers": [7, 0, 1, 3], "ret_unmatched_detections": [1, 3, 4]},
+{"matches_ext_com": [], "unmatched_detections": [1, 3], "unmatched_trackers": [2, 3], "matches": [[0, 0], [2, 5], [4, 1], [5, 4]], "unmatched_before": [[718.81, 381.02, 928.93, 1013.38, 0.73717], [1209.6, 449.36, 1241.09, 545.83, 0.74263]], "ret_unmatched_before": [[718.81, 381.02, 928.93, 1013.38, 0.73717], [1209.6, 449.36, 1241.09, 545.83, 0.74263]], "ret_matches": [[0, 0], [2, 5], [4, 1], [5, 4]], "ret_unmatched_trackers": [2, 3], "ret_unmatched_detections": [1, 3]},
+{"matches_ext_com": [], "unmatched_detections": [1, 3], "unmatched_trackers": [3, 6, 8, 0, 1], "matches": [[0, 2], [2, 5], [4, 4], [5, 7], [6, 9]], "unmatched_before": [[686.94, 446.86, 728.811, 574.47, 0.84872], [1046.0, 448.86, 1119.643, 671.79, 0.53011]], "ret_unmatched_before": [[686.94, 446.86, 728.811, 574.47, 0.84872], [1046.0, 448.86, 1119.643, 671.79, 0.53011]], "ret_matches": [[0, 2], [2, 5], [4, 4], [5, 7], [6, 9]], "ret_unmatched_trackers": [3, 6, 8, 0, 1], "ret_unmatched_detections": [1, 3]},
+{"matches_ext_com": [], "unmatched_detections": [1], "unmatched_trackers": [0], "matches": [[0, 2], [2, 4], [3, 1], [4, 3]], "unmatched_before": [[104.97, 442.87, 233.93, 831.75, 0.84747]], "ret_unmatched_before": [[104.97, 442.87, 233.93, 831.75, 0.84747]], "ret_matches": [[0, 2], [2, 4], [3, 1], [4, 3]], "ret_unmatched_trackers": [0], "ret_unmatched_detections": [1]},
+{"matches_ext_com": [], "unmatched_detections": [1], "unmatched_trackers": [1, 4, 0], "matches": [[0, 3], [2, 2], [3, 5]], "unmatched_before": [[558.32, 451.14, 578.755, 514.446, 0.33033]], "ret_unmatched_before": [[558.32, 451.14, 578.755, 514.446, 0.33033]], "ret_matches": [[0, 3], [2, 2], [3, 5]], "ret_unmatched_trackers": [1, 4, 0], "ret_unmatched_detections": [1]},
+{"matches_ext_com": [], "unmatched_detections": [1], "unmatched_trackers": [1, 7, 0], "matches": [[0, 6], [2, 5], [3, 3], [4, 2], [5, 4]], "unmatched_before": [[1674.4, 413.27, 1794.66, 776.04, 0.78249]], "ret_unmatched_before": [[1674.4, 413.27, 1794.66, 776.04, 0.78249]], "ret_matches": [[0, 6], [2, 5], [3, 3], [4, 2], [5, 4]], "ret_unmatched_trackers": [1, 7, 0], "ret_unmatched_detections": [1]},
+{"matches_ext_com": [], "unmatched_detections": [1], "unmatched_trackers": [1], "matches": [[0, 0], [2, 3], [3, 2]], "unmatched_before": [[534.0, 460.48, 555.974, 528.402, 0.78249]], "ret_unmatched_before": [[534.0, 460.48, 555.974, 528.402, 0.78249]], "ret_matches": [[0, 0], [2, 3], [3, 2]], "ret_unmatched_trackers": [1], "ret_unmatched_detections": [1]},
+{"matches_ext_com": [], "unmatched_detections": [1], "unmatched_trackers": [1], "matches": [[0, 0]], "unmatched_before": [[579.94, 451.29, 624.888, 588.13, 0.49629]], "ret_unmatched_before": [[579.94, 451.29, 624.888, 588.13, 0.49629]], "ret_matches": [[0, 0]], "ret_unmatched_trackers": [1], "ret_unmatched_detections": [1]},
+{"matches_ext_com": [], "unmatched_detections": [1], "unmatched_trackers": [1], "matches": [[0, 0]], "unmatched_before": [[687.71, 463.78, 761.3530000000001, 686.71, 0.44973]], "ret_unmatched_before": [[687.71, 463.78, 761.3530000000001, 686.71, 0.44973]], "ret_matches": [[0, 0]], "ret_unmatched_trackers": [1], "ret_unmatched_detections": [1]},
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diff --git a/spec/res/associate_detections_to_trackers.json b/spec/res/associate_detections_to_trackers.json
new file mode 100644
index 0000000..f579c1e
--- /dev/null
+++ b/spec/res/associate_detections_to_trackers.json
@@ -0,0 +1,599 @@
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diff --git a/spec/res/association__build_iou_matrix_ext.json b/spec/res/association__build_iou_matrix_ext.json
new file mode 100644
index 0000000..fc338d1
--- /dev/null
+++ b/spec/res/association__build_iou_matrix_ext.json
@@ -0,0 +1,162 @@
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diff --git a/spec/res/association__filter_low_iou_low_area.json b/spec/res/association__filter_low_iou_low_area.json
new file mode 100644
index 0000000..8857a28
--- /dev/null
+++ b/spec/res/association__filter_low_iou_low_area.json
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diff --git a/spec/res/association__occluded_tracker.json b/spec/res/association__occluded_tracker.json
new file mode 100644
index 0000000..779f41a
--- /dev/null
+++ b/spec/res/association__occluded_tracker.json
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diff --git a/spec/res/tracker__build_new_targets.json b/spec/res/tracker__build_new_targets.json
new file mode 100644
index 0000000..66a2375
--- /dev/null
+++ b/spec/res/tracker__build_new_targets.json
@@ -0,0 +1,335 @@
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diff --git a/spec/res/tracker__init_area_and_trackers.json b/spec/res/tracker__init_area_and_trackers.json
new file mode 100644
index 0000000..f212232
--- /dev/null
+++ b/spec/res/tracker__init_area_and_trackers.json
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diff --git a/spec/res/tracker__remove_outside_trackers.json b/spec/res/tracker__remove_outside_trackers.json
new file mode 100644
index 0000000..f247995
--- /dev/null
+++ b/spec/res/tracker__remove_outside_trackers.json
@@ -0,0 +1,335 @@
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diff --git a/spec/res/tracker_update.json b/spec/res/tracker_update.json
new file mode 100644
index 0000000..e8c0269
--- /dev/null
+++ b/spec/res/tracker_update.json
@@ -0,0 +1,330 @@
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47244.13736987644, 48380.105753454925, 46918.62628418121, 46604.7254980452, 39945.675495869524, 42276.388344540646, 42090.96540872055, 41912.60440267493, 41843.95973501963, 40766.97081207496, 47748.153114414155, 55744.890045175, 56124.962053241514, 47650.77195936019, 47849.145215178396, 46330.09212011282, 48425.949441690194, 49551.73234305009, 47203.7987550161, 43859.946927752346, 44807.904525242164, 48167.2671472216, 46316.96103936528, 44127.10991182834, 51063.37239601586, 51435.57929995052, 50483.674229266355, 55729.9564821064, 55607.34010490582, 62812.34323326364, 63558.05769836639, 66852.48173973642, 66059.76300842503, 64655.1264411128, 65045.532709472485, 66644.9220662642, 67107.75170800193, 74474.99740607243, 74750.26723740135, 75767.11281165518, 76133.4706092537, 75251.88217698653, 75166.23970992707, 75216.46689096665, 72684.51265503377, 69211.25630705862, 58520.676907773755, 60122.32610595256, 60729.29062081332, 58989.82019478766, 57902.96751171428, 59363.5961439048, 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72222.17871728404, 77996.85044679628, 79625.30303662278, 78890.43487030055, 83019.40581495919, 83045.60117393786, 83066.09982992926, 82963.12377814013, 82953.51934722943, 82761.33691794905, 81510.91762348903, 82215.43912005945, 81499.22997160033, 81108.71979844959, 81317.16296305742, 80058.31697996834, 79101.78846200695, 80727.62791108142, 77249.23395874604, 78646.07694431022, 79050.432521841, 89491.4054038358, 82259.8653890149, 83535.29820422469, 84714.73492197662, 85138.1769184271, 82698.18032736405, 83587.67547289742, 80757.07447521579], "conf_objt": 0.75, "conf_trgt": 0.35, "frame_count": 330, "max_age": 3, "min_hits": 3, "seq": "MOT17-02-DPM", "trackers": [{"time_since_observed": 7, "confidence": 1, "age": 329}, {"time_since_observed": 10, "confidence": 0.6799418323594753, "age": 247}, {"time_since_observed": 0, "confidence": 1, "age": 245}, {"time_since_observed": 5, "confidence": 0.5533193333094559, "age": 136}, {"time_since_observed": 0, "confidence": 0.5, "age": 110}, {"time_since_observed": 0, "confidence": 0.4519494196621088, "age": 15}], "unmatched": [[1463.5, 439.76, 1497.3220000000001, 543.23, 0.7487]], "unmatched_before": [[1463.5, 439.76, 1497.3220000000001, 543.23, 0.7487]], "unmatched_before_before": []}, "ret_ret": [[173.4355154115941, 418.79508750283253, 311.5312947477913, 835.0669538279503, 19.0], [891.6933188338694, 411.05946454173795, 1064.053234131422, 930.1493905216264, 15.0], [838.6739413271868, 446.6805154273054, 912.3399007533591, 669.6886316506345, 12.0], [1018.0465362414179, 384.9446150748134, 1224.1671553504952, 1005.3122989004416, 8.0], [1197.8204191561713, 416.50670477378895, 1283.5740636354117, 675.7471743021961, 7.0], [872.5590027327722, 230.00846884448805, 1114.6974014886962, 958.4302455470524, 2.0]], "ret_unmatched_trks_pos": [], "ret_unmatched_gts_pos": []}]}
diff --git a/tracker_app.py b/tracker_app.py
index f47e1ce..1ba215c 100644
--- a/tracker_app.py
+++ b/tracker_app.py
@@ -49,7 +49,7 @@ def parse_args():
     conf_objt = 0.75
     imgFolder = 'images/img_OH_EX_0.2_N_SEP_CONF_TOT_MIN_EXT_%s_%s' % (conf_trgt, conf_objt)     # Occlusion Handling + Area Cost /Extended Separate
     outFolder = 'outputs/output_OH_EX_0.2_N_SEP_CONF_TOT_MIN_%s_%s' % (conf_trgt, conf_objt)
-    mot_path = 'C:/Users/mhnas/Courses/Thesis/MOT/MOT17'
+    mot_path = 'MOT17'
     args = parse_args()
 
     total_time = 0.0
@@ -60,8 +60,16 @@ def parse_args():
 
     for seq in sequences:
         kalman_tracker.KalmanBoxTracker.count = 0   # Make zero ID number in the new sequence
-        mot_tracker = tracker.Sort_OH()  # create instance of the SORT with occlusion handling tracker
-        mot_tracker.seq = seq
+
+        if seq == 'MOT17-05-DPM' or seq == 'MOT17-05-FRCNN' or seq == 'MOT17-05-SDP' \
+           or seq == 'MOT17-05-POI' or seq == 'MOT17-06-DPM' or seq == 'MOT17-06-FRCNN' \
+           or seq == 'MOT17-06-SDP' or seq == 'MOT17-06-POI':
+            scene = np.array([640, 480])
+        else:
+            scene = np.array([1920, 1080])
+
+        mot_tracker = tracker.Sort_OH(scene=scene)  # create instance of the SORT with occlusion handling tracker
+        # mot_tracker.seq = seq
         mot_tracker.conf_trgt = conf_trgt
         mot_tracker.conf_objt = conf_objt
         common_path = ('%s/%s/%s' % (mot_path, phase, seq))