Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting

• URL: http://arxiv.org/abs/2505.21943v1
• Date: Wed, 28 May 2025 03:53:08 GMT
• Title: Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting
• Authors: Wei Lin, Chenyang Zhao, Antoni B. Chan,
• Abstract summary: Point detection has been developed to locate pedestrians in crowded scenes by training a counter through a point-to-point (P2P) supervision scheme.<n>We integrate point-based methods into a semi-supervised counting framework based on pseudo-labeling.<n>During implementation, the confidence for pseudo-labels fails to be propagated to background pixels via the P2P.<n>We propose a point-to-region (P2R) scheme to substitute P2P, which segments out local regions rather than detects a point corresponding to a pedestrian for supervision.
• Score: 49.165960263166966
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Point detection has been developed to locate pedestrians in crowded scenes by training a counter through a point-to-point (P2P) supervision scheme. Despite its excellent localization and counting performance, training a point-based counter still faces challenges concerning annotation labor: hundreds to thousands of points are required to annotate a single sample capturing a dense crowd. In this paper, we integrate point-based methods into a semi-supervised counting framework based on pseudo-labeling, enabling the training of a counter with only a few annotated samples supplemented by a large volume of pseudo-labeled data. However, during implementation, the training encounters issues as the confidence for pseudo-labels fails to be propagated to background pixels via the P2P. To tackle this challenge, we devise a point-specific activation map (PSAM) to visually interpret the phenomena occurring during the ill-posed training. Observations from the PSAM suggest that the feature map is excessively activated by the loss for unlabeled data, causing the decoder to misinterpret these over-activations as pedestrians. To mitigate this issue, we propose a point-to-region (P2R) scheme to substitute P2P, which segments out local regions rather than detects a point corresponding to a pedestrian for supervision. Consequently, pixels in the local region can share the same confidence with the corresponding pseudo points. Experimental results in both semi-supervised counting and unsupervised domain adaptation highlight the advantages of our method, illustrating P2R can resolve issues identified in PSAM. The code is available at https://github.com/Elin24/P2RLoss.

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