Vehicle Detetction and Tracking

This project is based on writing a software pipeline to detect and track vehicles in a video. Output is shown below:

The 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
• For those first two steps we normalize the features and randomize a selection for training and testing.
• Implement a sliding-window technique and used trained classifier to search for vehicles in images.
• Run pipeline on a video stream (first with the test_video.mp4 and later implemented 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.

Usage

The training data used to train the classifier can be found for vehicle and non-vehicle examples . These example images come from a combination of the GTI vehicle image database, the KITTI vision benchmark suite Cloning the repository and downloading the above files will make the notebook work

Files

• test_images contains example images for testing the pipeline on single frames.
• test_video.mp4 is a test video to check the performance of pipeline.
• test_video_output.mp4 is the result of pipeline on the test_video.mp4.
• project_video.mp4 is the main input video the pipeline works on.
• vehicle_detection_tracking files contain the output. the mp4 is the original video and gif is displayed above:

Notebook

The vehicle_detection_and_tracking.ipynb notebook goes through each of the steps laid out above with accompanying images