The steps of this project are the following:
- Augment training image set
- Perform a Histogram of Oriented Gradients (HOG) feature extraction on a labeled training set of images.
- Apply a color transform and append binned color features, as well as histograms of color, to your HOG feature vector.
- Train a classifier Linear SVM classifier
- Normalize features and randomize a selection for training and testing.
- Implement a sliding-window technique and use the trained classifier to search for vehicles in images.
- Run your pipeline on a video stream 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.
Here are links to the labeled data for vehicle and non-vehicle examples used to train my classifier. These example images come from a combination of the GTI vehicle image database, the KITTI vision benchmark suite, and examples extracted from the project video itself.
Some example images for testing my pipeline on single frames are located in the test_images folder. Examples of the output from each stage of my pipeline are located in the folder called ouput_images. Description of what each iamge shows is included in my writeup.
My pipeline is tested on the video called project_video.mp4. The output video is called output_project_video.mp4
As an optional challenge Once you have a working pipeline for vehicle detection, add in your lane-finding algorithm from the last project to do simultaneous lane-finding and vehicle detection!