Papers › PS-RCNN: Detecting Secondary Human Instances in a Crowd via Primary Object Suppression
PS-RCNN: Detecting Secondary Human Instances in a Crowd via Primary Object Suppression
Zheng Ge, Zequn Jie, Xin Huang, Rong Xu, Osamu Yoshie
Detecting human bodies in highly crowded scenes is a challenging problem. Two main reasons result in such a problem: 1). weak visual cues of heavily occluded instances can hardly provide sufficient information for accurate detection; 2). heavily occluded instances are easier to be suppressed by Non-Maximum-Suppression (NMS). To address these two issues, we introduce a variant of two-stage detectors called PS-RCNN. PS-RCNN first detects slightly/none occluded objects by an R-CNN module (referred as P-RCNN), and then suppress the detected instances by human-shaped masks so that the features of heavily occluded instances can stand out. After that, PS-RCNN utilizes another R-CNN module specialized in heavily occluded human detection (referred as S-RCNN) to detect the rest missed objects by P-RCNN. Final results are the ensemble of the outputs from these two R-CNNs. Moreover, we introduce a High Resolution RoI Align (HRRA) module to retain as much of fine-grained features of visible parts of the heavily occluded humans as possible. Our PS-RCNN significantly improves recall and AP by 4.49% and 2.92% respectively on CrowdHuman, compared to the baseline. Similar improvements on Widerperson are also achieved by the PS-RCNN.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Object Detection | CrowdHuman (full body) | PS-RCNN (Faster RCNN, ResNet50, COCO Instance Masks | AP | 87.94 | #15 of 19 | Archive leaderboard | report |
| Object Detection | CrowdHuman (full body) | PS-RCNN (Faster RCNN, ResNet50) | AP | 86.05 | #16 of 19 | Archive leaderboard | report |
| Object Detection | WiderPerson | PS-RCNN (Faster RCNN, ResNet50) | AP | 89.96 | #2 of 4 | 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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