Multi-person tracking-by-detection with local particle filtering and global occlusion handling

This paper presents a detection-based method for tracking an uncertain number of persons in complex scenarios with frequent occlusions. Frame-by-frame data association based particle filters are adopted to track targets in occlusion-free regions. When occlusion is detected, the associated trackers are deactivated and they are re-activated when the tracked persons are re-identified after occlusion. The re-identification problem is solved by global data association. And the association cost matrix only integrates information collected from the frames after occlusion to avoid tracking failure caused by false detections during occlusion. Furthermore, we improve the particle initialization by motion prediction and automatically configured dynamic model. Experimental results show that the proposed algorithm effectively reduces id switches and lost trajectories which happen frequently in local filtering methods. In the meantime, the algorithm is suitable for time-critical applications.