In this project, goal is to write a software pipeline to detect vehicles in a image or a video.
It can be achieved by following the below tasks:
- Perform a Histogram of Oriented Gradients (HOG) feature extraction on a labeled training set of images and train a classifier Linear SVM classifier.
- Implement a sliding-window technique and use your trained classifier to search for vehicles in images.
- Run 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 to train your classifier. These example images come from a combination of the GTI vehicle image database, KITTI vision benchmark suite, and examples extracted from the project video itself. You are welcome and encouraged to take advantage of the recently released Udacity labeled dataset to augment your training data.
The accuracy on the test set is above 97% with 8792 vehicle images and 8968 non-vihicle images.
Vehicle Detection - Udacity