Rear Vehicle Detection and Tracking for Lane Change Assist

A monocular vision based rear vehicle detection and tracking system is presented for Lane Change Assist (LCA), which does not need road boundary and lane information. Our algorithm extracts regions of interest (ROI) using the shadow underneath a vehicle, and accurately localizes vehicle regions in ROI by vehicle features such as symmetry, edge and shadow underneath vehicles. The algorithm realizes vehicle verification by combining knowledge-based and learning-based methods. During vehicle tracking, templates are dynamically created on-line, tracking window is adaptively adjusted with motion estimation, and confidence is determined for tracked vehicle. The algorithm was tested under various traffic scenes at different daytime, the result illustrated good performance.