Report

Vehicle Detection Project

The goals / steps of this project are the following:

• Perform a Histogram of Oriented Gradients (HOG) feature extraction on a labeled training set of images and train a classifier Linear SVM classifier
• Optionally, you can also apply a color transform and append binned color features, as well as histograms of color, to your HOG feature vector.
• Note: for those first two steps don't forget to normalize your features and randomize a selection for training and testing.
• Implement a sliding-window technique and use your trained classifier to search for vehicles in images.
• Run your pipeline on a video stream (start with the test_video.mp4 and later implement on full project_video.mp4) and create a heat map of recurring detections frame by frame to reject outliers and follow detected vehicles.
• Estimate a bounding box for vehicles detected.

Rubric Points

###Here I will consider the rubric points individually and describe how I addressed each point in my implementation.

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###Writeup / README

####1. Provide a Writeup / README that includes all the rubric points and how you addressed each one. You can submit your writeup as markdown or pdf. Here is a template writeup for this project you can use as a guide and a starting point.

You're reading it!

###Histogram of Oriented Gradients (HOG)

####1. Explain how (and identify where in your code) you extracted HOG features from the training images.

The code for this step is contained in the first part of the IPython notebook.

I started by reading in all the vehicle and non-vehicle images. Here is some examples of the vehicle and non-vehicle classes:

I then explored different color spaces and different skimage.hog() parameters (orientations, pixels_per_cell, and cells_per_block). I grabbed random images from each of the two classes and displayed them to get a feel for what the skimage.hog() output looks like.

####2. Explain how you settled on your final choice of HOG parameters.

I tried various combinations of parameters and compare the SVM classify results of each parameter.

At last, I choose to use

• cspace = 'HLS'
• orient = 9
• pix_per_cell = 8
• cell_per_block = 2
• hog_channel = 'ALL'

####3. Describe how (and identify where in your code) you trained a classifier using your selected HOG features (and color features if you used them).

I trained a linear SVM. And I combine HOG features and color features(both histogram of color & raw color) together. The test accuracy is around 99%.

###Sliding Window Search

####1. Describe how (and identify where in your code) you implemented a sliding window search. How did you decide what scales to search and how much to overlap windows?

The size of sliding window is 64(8x8 cells), and overlap is 75%(2 cells per step). I only search the window where y is between 400 ~ 656.

####2. Show some examples of test images to demonstrate how your pipeline is working. What did you do to optimize the performance of your classifier?

Ultimately I searched on two scales using HLS 3-channel HOG features plus spatially binned color and histograms of color in the feature vector, which provided a nice result. Here are some example images:

Video Implementation

####1. Provide a link to your final video output. Your pipeline should perform reasonably well on the entire project video (somewhat wobbly or unstable bounding boxes are ok as long as you are identifying the vehicles most of the time with minimal false positives.) Here's a link to my video result

####2. Describe how (and identify where in your code) you implemented some kind of filter for false positives and some method for combining overlapping bounding boxes.

I recorded the positions of positive detections in each frame of the video. From the positive detections I created a heatmap and then thresholded that map to identify vehicle positions. I then used scipy.ndimage.measurements.label() to identify individual blobs in the heatmap. I then assumed each blob corresponded to a vehicle. I constructed bounding boxes to cover the area of each blob detected.

I also combine 30 frames together. And I set the threshold=30. It means, if in previous 30 frames, if this I detect this car more than 30 times, then I think it's a car. Otherwise it may just a noise.

Here's an example result showing the heatmap from a series of frames of video, the result of scipy.ndimage.measurements.label() and the bounding boxes then overlaid on the last frame of video:

Here are six frames and their corresponding heatmaps:

Here the resulting bounding boxes are drawn onto the last frame in the series:

For this result, I just combine 6 frames together with threshold=4.

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###Discussion

####1. Briefly discuss any problems / issues you faced in your implementation of this project. Where will your pipeline likely fail? What could you do to make it more robust?

At first, I think classifier isn't important. I think classify between vehicle and others is a simple problem. But at last I found that classifier is very import. If you get too many false positive, you'll need more work to ignore them. A linear SVM may not good enough. Maybe a deep neural network may works better. But I didn't tried it yet.

Ignoring false positive took me a long time. At last, I use the current frame and the previous 30 frames together to remove false positive images. But there're still some false positive.