Pedestrian detection in highly crowded scenes using “online” dictionary learning for occlusion handling

Pedestrian detection is one of the most important task for video analytics of an intelligent surveillance system. In this paper, we propose a framework to improve the detection performance of a generic pedestrian detector for highly crowded scenes. The generic offline-trained pedestrian detectors usually cannot handle the problem of detecting pedestrians in highly crowded scenes due to the severe mutual occlusions of the pedestrians. In our approach, we firstly enhance the head detection and suppress the detections of other body parts in the deformable part-based model because the heads of pedestrians less likely to be occluded in highly crowded scenes. Then we propose to utilize multiple-instance dictionary learning to refine the previous detection responses. Compared to other related work, our approach builds a data-adaptive dictionary (codebook) for the heads of pedestrians, hence it can better handle the problem of detecting pedestrians in highly crowded scenes. The experiments on three datasets containing video clips of crowded scenes demonstrated the effectiveness of our proposed approach, significantly improving the state-of-the-art detector.