Papers › Repulsion Loss: Detecting Pedestrians in a Crowd
Repulsion Loss: Detecting Pedestrians in a Crowd
Xinlong Wang, Tete Xiao, Yuning Jiang, Shuai Shao, Jian Sun, Chunhua Shen
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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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Pedestrian Detection | Caltech | RepLoss + CityPersons dataset | Reasonable Miss Rate | 4.0 | #9 of 33 | Archive leaderboard | report |
| Pedestrian Detection | Caltech | RepLoss | Reasonable Miss Rate | 5.0 | #13 of 33 | Archive leaderboard | report |
| Pedestrian Detection | CityPersons | RepLoss | Bare MR^-2 | 7.6 | #17 of 22 | Archive leaderboard | report |
| Pedestrian Detection | CityPersons | RepLoss | Heavy MR^-2 | 56.9 | #17 of 22 | Archive leaderboard | report |
| Pedestrian Detection | CityPersons | RepLoss | Partial MR^-2 | 16.8 | #17 of 22 | Archive leaderboard | report |
| Pedestrian Detection | CityPersons | RepLoss | Reasonable MR^-2 | 13.2 | #17 of 22 | Archive leaderboard | report |
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