Tracking and Segmentation of Highway Vehicles in Cluttered and Crowded Scenes

Monitoring highway traffic is an important application of computer vision research. In this paper, we analyze congested highway situations where it is difficult to track individual vehicles in heavy traffic because vehicles either occlude each other or are connected together by shadow. Moreover, scenes from traffic monitoring videos are usually noisy due to weather conditions and/or video compression. We present a method that can separate occluded vehicles by tracking movements of feature points and assigning over-segmented image fragments to the motion vector that best represents the fragment´s movement. Experiments were conducted on traffic videos taken from highways in Turkey, and the proposed method can successfully separate vehicles in overpopulated and cluttered scenes.