Papers › Center and Scale Prediction: Anchor-free Approach for Pedestrian and Face Detection
Center and Scale Prediction: Anchor-free Approach for Pedestrian and Face Detection
Wei Liu, Irtiza Hasan, Shengcai Liao
Object detection generally requires sliding-window classifiers in tradition or anchor box based predictions in modern deep learning approaches. However, either of these approaches requires tedious configurations in boxes. In this paper, we provide a new perspective where detecting objects is motivated as a high-level semantic feature detection task. Like edges, corners, blobs and other feature detectors, the proposed detector scans for feature points all over the image, for which the convolution is naturally suited. However, unlike these traditional low-level features, the proposed detector goes for a higher-level abstraction, that is, we are looking for central points where there are objects, and modern deep models are already capable of such a high-level semantic abstraction. Besides, like blob detection, we also predict the scales of the central points, which is also a straightforward convolution. Therefore, in this paper, pedestrian and face detection is simplified as a straightforward center and scale prediction task through convolutions. This way, the proposed method enjoys a box-free setting. Though structurally simple, it presents competitive accuracy on several challenging benchmarks, including pedestrian detection and face detection. Furthermore, a cross-dataset evaluation is performed, demonstrating a superior generalization ability of the proposed method. Code and models can be accessed at (https://github.com/liuwei16/CSP and https://github.com/hasanirtiza/Pedestron).
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Code
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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 | CSP + CityPersons dataset | Reasonable Miss Rate | 3.8 | #8 of 33 | Archive leaderboard | report |
| Pedestrian Detection | Caltech | CSP | Reasonable Miss Rate | 4.5 | #11 of 33 | Archive leaderboard | report |
| Pedestrian Detection | CityPersons | CSP (with offset) + ResNet-50 | Bare MR^-2 | 7.3 | #14 of 22 | Archive leaderboard | report |
| Pedestrian Detection | CityPersons | CSP (with offset) + ResNet-50 | Heavy MR^-2 | 49.3 | #14 of 22 | Archive leaderboard | report |
| Pedestrian Detection | CityPersons | CSP (with offset) + ResNet-50 | Large MR^-2 | 6.5 | #14 of 22 | Archive leaderboard | report |
| Pedestrian Detection | CityPersons | CSP (with offset) + ResNet-50 | Medium MR^-2 | 3.7 | #14 of 22 | Archive leaderboard | report |
| Pedestrian Detection | CityPersons | CSP (with offset) + ResNet-50 | Partial MR^-2 | 10.4 | #14 of 22 | Archive leaderboard | report |
| Pedestrian Detection | CityPersons | CSP (with offset) + ResNet-50 | Reasonable MR^-2 | 11.0 | #14 of 22 | Archive leaderboard | report |
| Pedestrian Detection | CityPersons | CSP (with offset) + ResNet-50 | Small MR^-2 | 16.0 | #14 of 22 | Archive leaderboard | report |
| Pedestrian Detection | CityPersons | CSP (with offset) + ResNet-50 | Test Time | 0.33s/img | #14 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.
Methods
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