Papers › CrowdHuman: A Benchmark for Detecting Human in a Crowd
CrowdHuman: A Benchmark for Detecting Human in a Crowd
Shuai Shao, Zijian Zhao, Boxun Li, Tete Xiao, Gang Yu, Xiangyu Zhang, Jian Sun
Human detection has witnessed impressive progress in recent years. However, the occlusion issue of detecting human in highly crowded environments is far from solved. To make matters worse, crowd scenarios are still under-represented in current human detection benchmarks. In this paper, we introduce a new dataset, called CrowdHuman, to better evaluate detectors in crowd scenarios. The CrowdHuman dataset is large, rich-annotated and contains high diversity. There are a total of 470K human instances from the train and validation subsets, and 22.6 persons per image, with various kinds of occlusions in the dataset. Each human instance is annotated with a head bounding-box, human visible-region bounding-box and human full-body bounding-box. Baseline performance of state-of-the-art detection frameworks on CrowdHuman is presented. The cross-dataset generalization results of CrowdHuman dataset demonstrate state-of-the-art performance on previous dataset including Caltech-USA, CityPersons, and Brainwash without bells and whistles. We hope our dataset will serve as a solid baseline and help promote future research in human detection tasks.
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Object Detection | CrowdHuman (full body) | Faster RCNN (ResNet50) | AP | 84.95 | #17 of 19 | Archive leaderboard | report |
| Object Detection | CrowdHuman (full body) | Faster RCNN (ResNet50) | mMR | 50.49 | #17 of 19 | Archive leaderboard | report |
| Pedestrian Detection | Caltech | FRCNN+FPN-Res50+refined feature map+Crowdhuman | Reasonable Miss Rate | 3.46 | #7 of 33 | Archive leaderboard | report |
| Pedestrian Detection | CityPersons | FRCNN+FPN-Res50+refined feature map+Crowdhuman | Reasonable MR^-2 | 10.67 | #12 of 22 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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