2,407 research outputs found
Repulsion Loss: Detecting Pedestrians in a Crowd
Detecting individual pedestrians in a crowd remains a challenging problem
since the pedestrians often gather together and occlude each other in
real-world scenarios. In this paper, we first explore how a state-of-the-art
pedestrian detector is harmed by crowd occlusion via experimentation, providing
insights into the crowd occlusion problem. Then, we propose a novel bounding
box regression loss specifically designed for crowd scenes, termed repulsion
loss. This loss is driven by two motivations: the attraction by target, and the
repulsion by other surrounding objects. The repulsion term prevents the
proposal from shifting to surrounding objects thus leading to more crowd-robust
localization. Our detector trained by repulsion loss outperforms all the
state-of-the-art methods with a significant improvement in occlusion cases.Comment: Accepted to IEEE Conference on Computer Vision and Pattern
Recognition (CVPR) 201
- …