Learning Mutual Visibility Relationship for Pedestrian Detection with a Deep Model
Detecting pedestrians in cluttered scenes is a challenging problem in computer vision. The difficulty is added when several pedestrians overlap in images and occlude each other. We observe, however, that the occlusion/visibility statuses of overlapping pedestrians provide useful mutual relationship for visibility estimation the visibility estimation of one pedestrian facilitates the visibility estimation of another. In this paper, we propose a mutual visibility deep model that jointly estimates the visibility statuses of overlapping pedestrians. The visibility relationship among pedestrians is learned from the deep model for recognizing co existing pedestrians. Then the evidence of co existing pedestrians is used for improving the single pedestrian detection results. Compared with existing image based pedestrian detection approaches, our approach has the lowest average miss rate on the Caltech Train dataset and the ETH dataset. Experimental results show that the mutual visibility deep model effectively improves the pedestrian detection results. The mutual visibility deep model leads to 6 15 improvements on multiple benchmark datasets.
International Journal of Computer Vision