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arXiv · 1711.07752

Repulsion Loss: Detecting Pedestrians in a Crowd

Xinlong Wang·Tete Xiao·Yuning Jiang·Shuai Shao·Jian Sun·Chunhua Shen
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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 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.

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Xinlong Wang, Tete Xiao, Yuning Jiang, Shuai Shao, Jian Sun, Chunhua Shen. 2017-11-21. Repulsion Loss: Detecting Pedestrians in a Crowd. https://arxiv.org/abs/1711.07752

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