diff --git a/examples/python/detect_and_track_objects/README.md b/examples/python/detect_and_track_objects/README.md
index 4e4dfc7ab137..b42feeb5d6e7 100644
--- a/examples/python/detect_and_track_objects/README.md
+++ b/examples/python/detect_and_track_objects/README.md
@@ -17,7 +17,7 @@ Visualize object detection and segmentation using the [Huggingface's Transformer
 </picture>
 
 ## Used Rerun types
-[`Image`](https://www.rerun.io/docs/reference/types/archetypes/image), [`SegmentationImage`](https://www.rerun.io/docs/reference/types/archetypes/segmentation_image), [`AnnotationContext`](https://www.rerun.io/docs/reference/types/archetypes/annotation_context), [`Boxes2D`](https://www.rerun.io/docs/reference/types/archetypes/boxes2d), [`TextLog`](https://www.rerun.io/docs/reference/types/archetypes/text_log)
+[`Image`](https://www.rerun.io/docs/reference/types/archetypes/image), [`AssetVideo`](https://www.rerun.io/docs/reference/types/archetypes/asset_video), [`VideoFrameReference`](https://rerun.io/docs/reference/types/archetypes/video_frame_reference), [`SegmentationImage`](https://www.rerun.io/docs/reference/types/archetypes/segmentation_image), [`AnnotationContext`](https://www.rerun.io/docs/reference/types/archetypes/annotation_context), [`Boxes2D`](https://www.rerun.io/docs/reference/types/archetypes/boxes2d), [`TextLog`](https://www.rerun.io/docs/reference/types/archetypes/text_log)
 
 ## Background
 In this example, CSRT (Channel and Spatial Reliability Tracker), a tracking API introduced in OpenCV, is employed for object detection and tracking across frames.
@@ -36,12 +36,21 @@ rr.set_time_sequence("frame", frame_idx)
 ```
 
 ### Video
-The input video is logged as a sequence of [`Image`](https://www.rerun.io/docs/reference/types/archetypes/image) to the `image` entity.
+The input video is logged as a static [`AssetVideo`](https://www.rerun.io/docs/reference/types/archetypes/asset_video) to the `video` entity.
+
+```python
+video_asset = rr.AssetVideo(path=video_path)
+frame_timestamps_ns = video_asset.read_frame_timestamps_ns()
+
+rr.log("video", video_asset, static=True)
+```
+
+Each frame is processed and the timestamp is logged to the `frame` timeline using a [`VideoFrameReference`](https://www.rerun.io/docs/reference/types/archetypes/video_frame_reference).
 
 ```python
 rr.log(
-    "image",
-    rr.Image(rgb).compress(jpeg_quality=85)
+    "video",
+    rr.VideoFrameReference(nanoseconds=frame_timestamps_ns[frame_idx])
 )
 ```
 
diff --git a/examples/python/detect_and_track_objects/detect_and_track_objects.py b/examples/python/detect_and_track_objects/detect_and_track_objects.py
index b3028116c060..d9be4f0e7ce6 100755
--- a/examples/python/detect_and_track_objects/detect_and_track_objects.py
+++ b/examples/python/detect_and_track_objects/detect_and_track_objects.py
@@ -205,7 +205,7 @@ def update(self, bgr: cv2.typing.MatLike) -> None:
     def log_tracked(self) -> None:
         if self.is_tracking:
             rr.log(
-                f"image/tracked/{self.tracking_id}",
+                f"video/tracked/{self.tracking_id}",
                 rr.Boxes2D(
                     array=self.tracked.bbox_xywh,
                     array_format=rr.Box2DFormat.XYWH,
@@ -213,7 +213,7 @@ def log_tracked(self) -> None:
                 ),
             )
         else:
-            rr.log(f"image/tracked/{self.tracking_id}", rr.Clear(recursive=False))  # TODO(#3381)
+            rr.log(f"video/tracked/{self.tracking_id}", rr.Clear(recursive=False))  # TODO(#3381)
 
     def update_with_detection(self, detection: Detection, bgr: cv2.typing.MatLike) -> None:
         self.num_recent_undetected_frames = 0
@@ -339,6 +339,11 @@ def track_objects(video_path: str, *, max_frame_count: int | None) -> None:
     detector = Detector(coco_categories=coco_categories)
     logging.info("Detector initialized.")
 
+    video_asset = rr.AssetVideo(path=video_path)
+    frame_timestamps_ns = video_asset.read_frame_timestamps_ns()
+
+    rr.log("video", video_asset, static=True)
+
     logging.info("Loading input video: %s", str(video_path))
     cap = cv2.VideoCapture(video_path)
     frame_idx = 0
@@ -358,7 +363,7 @@ def track_objects(video_path: str, *, max_frame_count: int | None) -> None:
             break
 
         rgb = cv2.cvtColor(bgr, cv2.COLOR_BGR2RGB)
-        rr.log("image", rr.Image(rgb).compress(jpeg_quality=85))
+        rr.log("video", rr.VideoFrameReference(nanoseconds=frame_timestamps_ns[frame_idx]))
 
         if not trackers or frame_idx % 40 == 0:
             detections = detector.detect_objects_to_track(rgb=rgb, frame_idx=frame_idx)