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.

Data Exploration

• Number of vehicle images: 8792
• Number non-vehicle images: 8968

First, I explored 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.

Here is an example using the RGB color space and HOG parameters of orientations=9 , pixels_per_cell=(8, 8) and cells_per_block=(2, 2) :

I also visualized the color histograms and the spatial bins (RGB):

I tried various combinations of parameters (code cell for all features on a sample of 1000 and then 2000 images from vehicles and non vehicles set. The best fit with over 99% accuracy was achieved with the following params:

• color_space='YCrCb'
• spatial_size=(32, 32)
• hist_bins=32
• orient=9
• pix_per_cell=8
• cell_per_block=2
• hog_channel='ALL’

Classifier

I trained a linear SVM with a feature vector that combined spatially binned features, color histogram features and hog features (code cell 11) and got an accuracy of over 99% on the test set:

• 41.66 seconds to extract vehicle features...
• 40.87 seconds to extract non-vehicle features...
• Feature vector length: 8460
• 22.3 seconds to train SVC..
• Test accuracy of SVC: 0.9904

Sliding Window Search

With overlap = 0.5 and xy_window as (64, 64) i got the following results on the test images:

It searched over 800 windows (some needlessly) and missed the white cars in several of these images and finds cars in tree. So next I edited the y crop to not include the tree and sky portion and set xy_window to 128. The results:

There are several false positives and it misses the cars that are far away (smaller scale)

Single scale (1.5) window search without thresholding and without using scipy.ndimage.measurements.label() to identify individual blobs in heat maps yielded these results:

Ultimately I searched on multi scales (1 to 3.5) using YCrCb 3-channel HOG features plus spatially binned color and histograms of color in the feature vector, which provided a nice result. I also added heat map calculations and thresholding the heat map for better results (cell 14). Here are some example images:

Final Result

Here's a link to my video result

False Positives & 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.

Failure Points & Enhancements

Using multi scale window search is a little slow and could be made faster by averaging over a few frames the position of a vehicle. But calculation over multiple scales does rule out almost all false positives. Another possible speedup is to search using smaller scale values near the horizon (just below) y values and larger as we get closer to the car.