Hypergraph Association Weakly Supervised Crowd Counting

Author:

Li Bo1ORCID,Zhang Yong1ORCID,Zhang Chengyang1ORCID,Piao Xinglin1ORCID,Yin Baocai1ORCID

Affiliation:

1. Beijing University of Technology

Abstract

Weakly supervised crowd counting involves the regression of the number of individuals present in an image, using only the total number as the label. However, this task is plagued by two primary challenges: the large variation of head size and uneven distribution of crowd density. To address these issues, we propose a novel Hypergraph Association Crowd Counting (HACC) framework. Our approach consists of a new multi-scale dilated pyramid module that can efficiently handle the large variation of head size. Further, we propose a novel hypergraph association module to solve the problem of uneven distribution of crowd density by encoding higher-order associations among features, which opens a new direction to solve this problem. Experimental results on multiple datasets demonstrate that our HACC model achieves new state-of-the-art results.

Funder

National Key R&D Program of China

National Natural Science Foundation of China

Beijing Natural Science Foundation

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Networks and Communications,Hardware and Architecture

Reference84 articles.

1. Shahira Abousamra, Minh Hoai, Dimitris Samaras, and Chao Chen. 2021. Localization in the crowd with topological constraints. In Proceedings of the AAAI Conference on Artificial Intelligence. 872–881.

2. James Atwood and Don Towsley. 2016. Diffusion-convolutional neural networks. In Proceedings of Advances in Neural Information Processing Systems, Vol. 29.

3. Deepak Babu Sam, Shiv Surya, and R. Venkatesh Babu. 2017. Switching convolutional neural network for crowd counting. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. 5744–5752.

4. Intelligent vehicle counting and classification sensor for real-time traffic surveillance;Balid Walid;IEEE Transactions on Intelligent Transportation Systems,2017

5. Lokesh Boominathan, Srinivas S. S. Kruthiventi, and R. Venkatesh Babu. 2016. CrowdNet: A deep convolutional network for dense crowd counting. In Proceedings of the ACM International Conference on Multimedia. 640–644.

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