Vehicle Segmentation and Tracking in the Presence of Occlusions

Abstract

A novel method is presented for visually monitoring a highway automatically when the camera is relatively low to the ground and on the side of the road. In such a case, occlusion and the perspective effects that are due to the heights of the vehicles cannot be ignored. By using a single camera, the system automatically detects and tracks feature points throughout the image sequence, estimates the 3-D world coordinates of the points on the vehicles, and groups those points together to segment and track the individual vehicles. Experimental results on different highways demonstrate the ability of the system to segment and track vehicles even in the presence of severe occlusion and significant perspective changes. By handling perspective effects, the approach overcomes a limitation of commercially available machine vision-based traffic-monitoring systems that are used in many intelligent transportation systems (ITS) applications. Researchers are targeting this system as a step toward a next-generation ITS sensor for automated traffic analysis.