Papers › Adaptive NMS: Refining Pedestrian Detection in a Crowd
Adaptive NMS: Refining Pedestrian Detection in a Crowd
Songtao Liu, Di Huang, Yunhong Wang
Pedestrian detection in a crowd is a very challenging issue. This paper addresses this problem by a novel Non-Maximum Suppression (NMS) algorithm to better refine the bounding boxes given by detectors. The contributions are threefold: (1) we propose adaptive-NMS, which applies a dynamic suppression threshold to an instance, according to the target density; (2) we design an efficient subnetwork to learn density scores, which can be conveniently embedded into both the single-stage and two-stage detectors; and (3) we achieve state of the art results on the CityPersons and CrowdHuman benchmarks.
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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) | Adaptive NMS (Faster RCNN, ResNet50) | AP | 84.71 | #18 of 19 | Archive leaderboard | report |
| Object Detection | CrowdHuman (full body) | Adaptive NMS (Faster RCNN, ResNet50) | mMR | 49.73 | #18 of 19 | 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.
Methods
Introduced by this paper: Adaptive NMS
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