Combining multiple tracking modalities for vehicle tracking at traffic intersections

This paper presents a camera-based system for tracking vehicles at outdoor scenes such as traffic intersections. Two different computer vision modalities, namely, the connected regions obtained through region segmentation and color analysis, obtained through a mean-shift tracking procedure are combined sequentially using an extended Kalman filter to provide the position of each target. Data association ambiguities arising in blob tracking are handled by using oriented bounding boxes and a joint probabilistic data association filter. We show that the above tracking formulation can provide reasonable tracking despite the stop-and-go motion of vehicles and clutter in traffic intersections.