Artificial potential field based cooperative particle filter for multi-view multi-object tracking

To continuously track the multiple occluded object in the crowded scene, we propose a new multi-view multi-object tracking method basing on artificial potential field and cooperative particle filter in which we combine the bottom-up and top-down tracking methods for better tracking results. After obtaining the accurate occupancy map through the multi-planar consistent constraint, we predict the tracking probability map via cooperation among multiple particle filters. The main point is that multiple particle filters' cooperation is considered as the path planning and particles' random shifting is guided by the artificial potential field. Comparative experimental results with the traditional blob-detection-tracking algorithm demonstrate the effectiveness and robustness of our method.