Model-Based Object Tracking in Traffic Scenes

Abstract

This contribution addresses the problem of detection and tracking of moving vehicles in image sequences from traffic scenes recorded by a stationary camera. In order to exploit the a priori knowledge about the shape and the physical motion of vehicles in traffic scenes, a parameterized vehicle model is used for an intraframe matching process and a recursive estimator based on a motion model is used for motion estimation. The initial guess about the position and orientation for the models are computed with the help of a clustering approach of moving image features. Shadow edges of the models are taken into account in the matching process. This enables tracking of vehicles under complex illumination conditions and within a small effective field of view. Results on real world traffic scenes are presented and open problems are outlined.