Local Segmentation for Pedestrian Tracking in Dense Crowds

Abstract

People tracking in dense crowds is challenging due to the high levels of inter-pedestrian occlusions occurring continuously. After each successive occlusion, the surface of the tracked object that has never been hidden reduces. If not corrected, this shrinking problem eventually causes the system to stop as the area to track become too small. In this paper we investigate how hidden parts of one target object can be recovered after occlusions and propose challenging data to evaluate such segmentation-tracking technique in dense crowds. The segmentation/tracking problem is particularly difficult to solve for non-rigid objects. Here, we focus on pedestrians whose limbs and lower body parts often get occluded in crowded scene. We first investigate the unmet challenges of pedestrian tracking in crowds and propose a challenging video to evaluate segmentation-tracking robustness to inter-pedestrian occlusions. We then detail a fast segmentation-based method to overcome some aspects of the tracking-under-occlusion problem. We finally compare our results with two existing tracking methods.