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 this https URL.
Abstract (translated)
多视角人群计数可以有效缓解单图像人群计数中常见的遮挡问题。现有的基于深度学习的多视角人群计数方法将不同摄像头视角的图像投影到一个公共空间，以获取地面密度图，这需要大量的且成本高昂的人群标注和相机校准工作。因此，有人提出了无需相机校准和场景级人群注释的方法（即无校准方法）。然而，现有的无校准方法仍然需要昂贵的图像级别的人群注释来训练单视角计数模块。为此，在本文中，我们提出了一种弱监督下的无校准多视角人群计数方法（WSCF-MVCC），直接使用人群数量作为单视角计数模块的监督信号，而不是基于人群标注构建的密度图。相反，该方法利用了自监督排序损失来增强模型的感知能力，并且无需额外的注释成本，该损失函数利用多尺度先验信息。此外，所提出的模型还借助语义信息实现了更准确的视角匹配，并因此得出了更为精确的场景级别人群计数估计。在三个广泛使用的多视图计数组数据集上，在弱监督设置下，提出的方法超过了现有方法的表现，这表明与校准方法相比，该方法更适合实际部署。代码发布在此链接：[此URL]（请将此处的实际内容替换为正确的URL）。
URL
https://arxiv.org/abs/2512.02359
