Part-based Deep Network for Pedestrian Detection in Surveillance Videos

Accurate pedestrian detection in highly crowded surveillance videos is a challenging task, since the regions of pedestrians in the videos may be largely occluded by other pedestrians. In this paper, we propose an effective part-based deep network cascade (HsNet) to solve this problem. In this model, the part-based scheme effectively restrains the appearance variations of pedestrians caused by heavy occlusion. The deep network captures discriminative information of visible body parts. In addition, the cascade architecture enables very fast detection. We make experiments on one of the largest surveillance video dataset, namely TRECVid SED Pedestrian Dataset (SED-PD). It is shown that in highly crowded surveillance videos, our proposed method achieves very competitive performance compared with state-of-the-art methods. More importantly, our method is significantly faster.