Papers › Cross-Scene Crowd Counting via Deep Convolutional Neural Networks
Cross-Scene Crowd Counting via Deep Convolutional Neural Networks
Cong Zhang, Hongsheng Li, Xiaogang Wang, Xiaokang Yang
Cross-scene crowd counting is a challenging task where no laborious data annotation is required for counting people in new target surveillance crowd scenes unseen in the training set. The performance of most existing crowd counting methods drops significantly when they are applied to an unseen scene. To address this problem, we propose a deep convolutional neural network (CNN) for crowd counting, and it is trained alternatively with two related learning objectives, crowd density and crowd count. This proposed switchable learning approach is able to obtain better local optimum for both objectives. To handle an unseen target crowd scene, we present a data-driven method to fine-tune the trained CNN model for the target scene. A new dataset including 108 crowd scenes with nearly 200,000 head annotations is introduced to better evaluate the accuracy of cross-scene crowd counting methods. Extensive experiments on the proposed and another two existing datasets demonstrate the effectiveness and reliability of our approach.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Crowd Counting | ShanghaiTech A | Zhang et al. | MAE | 181.8 | #35 of 35 | Archive leaderboard | report |
| Crowd Counting | ShanghaiTech B | Zhang et al. | MAE | 32.0 | #32 of 32 | Archive leaderboard | report |
| Crowd Counting | UCF CC 50 | Zhang et al. | MAE | 467.0 | #22 of 22 | Archive leaderboard | report |
| Crowd Counting | WorldExpo’10 | Zhang et al. | Average MAE | 12.9 | #15 of 15 | 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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