On-road obstacle detection and tracking system using robust global stereo vision method

Abstract

In this paper, we present a visual obstacle detection and tracking system based on a dense stereo vision method. We combine a global stereo matcher with a correlation based cost function for generating a reliable disparity-map. An NCC algorithm is robust to illumination variation, and a BP based global disparity computation algorithm is efficient for recovering the disparity information of a large textureless area in real driving scenes. Then an obstacle detector and a tracker module are implemented and tested under actual driving conditions. Using U-V disparity representation, a road profile is efficiently extracted, and obstacle ROI can be detected. In the process of obstacle detection, a few heuristic constraints are applied to exclude wrong candidates, and a further verification step is proceeded by a tracker. Implemented system offers accurate and reliable range images under various noisy imaging conditions, which results in robust detection and tracking performance.