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
- Apply a color transform and append binned color features, as well as histograms of color, to HOG feature vector.
- Normalize features and randomize a selection for training and testing.
- Implement a sliding-window technique and use trained classifier to search for vehicles in images.
- Run 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.
Entire vehicle detection pipeline is in the IPython notebook vehicle_detection.ipynb. Supporting lane lines detection code is in subdir ./lanelines
