Computer Vision - Road Tracking

December 2017

This project explored the use of computer vision and image processing techniques to identify the roads and any pedestrians in a sequence of images taken from a video from a car driving around Durham. The motivation is to explore modern techniques for self-driving cars to figure out where they are allowed to drive.

Structure:

The basic outline of the algorithm is as follows:

Pre-filter the images to mask the sky and correct the colours by converting to different colour spaces
Identify the Region-Of-Interest (ROI) where the road is most likely to be
Use RANSAC to find the best estimate of the 3D plane on which the road lies
Use any technique to draw the 3D plane on the image
Optional: Use a feature detector (such as HOG) to identify pedestrians and draw a box around them

If you would like more detail, including intermediate stages, look at the report at the end of this page.

What I Learnt:

How to use the OpenCV python library to perform image processing/computer vision techniques on images and videos
How to use state-of-the-art computer vision algorihtms to solve a real-world problem

Examples:

A video showing the road-detection software running. Note: the solution is not always perfect but the road is found most of the time. The red line indicates an estimate of the road plane and the blue-outlined area represents a more refined estimate of where the road is.