Abstract
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 specifi-
cally 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 the state-ofthe-art methods with a significant improvement in occlusion
cases.