This is the fifth project I am doing as part of Udacity's Self-Driving-Car Nanodegree.
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.
- Implement a sliding-window technique and use your 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.
For a more detailed insight on the project please see the full Writeup / Report.
The images used to train the linear classifier are a mix from various datasets which are listed below: