Vehicle tracking iterative by Kalman-based constrained multiple-kernel and 3-D model-based localization

In this paper, we propose a novel vehicle tracking system under a surveillance camera. The proposed system tracks vehicles by using constrained multiple-kernel facilitated with Kalman filtering, and then continuously updates the position and the orientation by adopting a systematically built 3-D vehicle model in an evolutionary computing framework. The proposed system can thus successfully track vehicles under occlusion as facilitated by the obtained 3-D geometry of vehicles. Experimental results have shown the favorable performance of the proposed system, which can successfully track vehicles while maintaining the knowledge of 3-D vehicle geometry.