A moving vehicle segmentation method based on clustering of feature points for tracking at urban intersection

Abstract

Video-based moving vehicle detection and tracking are important parts of modern intelligent transportation system (ITS). They can provide valuable information such as vehicle velocity and trajectory for ITS. However, vehicle tracking at urban intersection is more challenging than that at highway, due to the complicated scenarios, such as the variety of vehicle moving direction, inter-vehicle clustering and occlusion. Many successful vehicle tracking systems developed for high way vehicle tracking based on the blob-tracking approach failed to provide acceptable performance at urban intersections when there are heavy vehicle occlusion or vehicles close to each other. This paper proposes a novel vehicle segmentation method for moving vehicle segmentation at urban intersection by seeking the spatial-temporal matching of feature points. Experimental results show that feature point may be taken as an important cue for moving vehicle segmentation and tracking under sophisticated traffic situation.