Papers › Beta R-CNN: Looking into Pedestrian Detection from Another Perspective
Beta R-CNN: Looking into Pedestrian Detection from Another Perspective
Zixuan Xu, Banghuai Li, Ye Yuan, Anhong Dang
Recently significant progress has been made in pedestrian detection, but it remains challenging to achieve high performance in occluded and crowded scenes. It could be attributed mostly to the widely used representation of pedestrians, i.e., 2D axis-aligned bounding box, which just describes the approximate location and size of the object. Bounding box models the object as a uniform distribution within the boundary, making pedestrians indistinguishable in occluded and crowded scenes due to much noise. To eliminate the problem, we propose a novel representation based on 2D beta distribution, named Beta Representation. It pictures a pedestrian by explicitly constructing the relationship between full-body and visible boxes, and emphasizes the center of visual mass by assigning different probability values to pixels. As a result, Beta Representation is much better for distinguishing highly-overlapped instances in crowded scenes with a new NMS strategy named BetaNMS. What's more, to fully exploit Beta Representation, a novel pipeline Beta R-CNN equipped with BetaHead and BetaMask is proposed, leading to high detection performance in occluded and crowded scenes.
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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) | Beta R-CNN | AP | 89.6 | #12 of 19 | Archive leaderboard | report |
| Object Detection | CrowdHuman (full body) | Beta R-CNN | mMR | 40.3 | #12 of 19 | Archive leaderboard | report |
| Pedestrian Detection | CityPersons | Beta R-CNN | Bare MR^-2 | 6.4 | #11 of 22 | Archive leaderboard | report |
| Pedestrian Detection | CityPersons | Beta R-CNN | Heavy MR^-2 | 47.1 | #11 of 22 | Archive leaderboard | report |
| Pedestrian Detection | CityPersons | Beta R-CNN | Partial MR^-2 | 10.3 | #11 of 22 | Archive leaderboard | report |
| Pedestrian Detection | CityPersons | Beta R-CNN | Reasonable MR^-2 | 10.6 | #11 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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