WSCF-MVCC: Weakly-supervised Calibration-free Multi-view Crowd Counting

• URL: http://arxiv.org/abs/2512.02359v1
• Date: Tue, 02 Dec 2025 03:07:22 GMT
• Title: WSCF-MVCC: Weakly-supervised Calibration-free Multi-view Crowd Counting
• Authors: Bin Li, Daijie Chen, Qi Zhang,
• Abstract summary: Multi-view crowd counting can effectively mitigate occlusion issues that commonly arise in single-image crowd counting.<n>Existing deep-learning multi-view crowd counting methods project different camera view images onto a common space to obtain ground-plane density maps.<n>We propose a weakly-supervised calibration-free multi-view crowd counting method.
• Score: 10.758049886638721
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Multi-view crowd counting can effectively mitigate occlusion issues that commonly arise in single-image crowd counting. Existing deep-learning multi-view crowd counting methods project different camera view images onto a common space to obtain ground-plane density maps, requiring abundant and costly crowd annotations and camera calibrations. Hence, calibration-free methods are proposed that do not require camera calibrations and scene-level crowd annotations. However, existing calibration-free methods still require expensive image-level crowd annotations for training the single-view counting module. Thus, in this paper, we propose a weakly-supervised calibration-free multi-view crowd counting method (WSCF-MVCC), directly using crowd count as supervision for the single-view counting module rather than density maps constructed from crowd annotations. Instead, a self-supervised ranking loss that leverages multi-scale priors is utilized to enhance the model's perceptual ability without additional annotation costs. What's more, the proposed model leverages semantic information to achieve a more accurate view matching and, consequently, a more precise scene-level crowd count estimation. The proposed method outperforms the state-of-the-art methods on three widely used multi-view counting datasets under weakly supervised settings, indicating that it is more suitable for practical deployment compared with calibrated methods. Code is released in https://github.com/zqyq/Weakly-MVCC.

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