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Cross-Modal Information Aggregation and Distribution Method for Crowd Counting

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Advances in Computer Graphics (CGI 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14498))

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Abstract

Crowd counting is a fundamental and challenging task in computer vision. However, existing methods are relatively limited in dealing with scale and illumination changes simultaneously. To improve the accuracy of crowd counting and address the challenges of illumination and scale changes, we adopt the concept of crowding degree information. Due to the fact that a count map can accurately obtain the population in an image and solve the occlusion problem, we use the count map as a specific form of crowding degree information and propose a new cross-modal information aggregation and distribution model for crowd counting. We first input the crowding degree information into LibraNet and modify it with Information Aggregation Transfer (IAT) and Information Distribution Transfer (IDT) modules to obtain a count map. Then, light information, thermal information and crowding degree information are respectively input into the network through RGB image, themal image, and count map. A more accurate density map can be obtained through multiple convolution operations and IADM processing to improve counting accuracy. Finally, the density map is integrated to obtain the number of people. Experiments demonstrate that our methods provide superior quality and higher parallelism. Therefore, we can obtain higher-accuracy density maps by using light information, thermal information, and crowding degree information.

Supported by the National Key R &D Program of China under Grant No. 2021ZD0200403 and 2018YFB1404102.

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Acknowledgements

This research was supported by STI 2030-Major Projects 2021ZD0200400 and the National Key R &D Program of China under Grant No. 2018YFB1404102.

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Authors and Affiliations

  1. College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, Zhejiang, China

    Yin Chen, Yuhao Zhou & Tianyang Dong

Authors
  1. Yin Chen
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  2. Yuhao Zhou
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  3. Tianyang Dong
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Corresponding author

Correspondence to Tianyang Dong .

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Editors and Affiliations

  1. Shanghai Jiao Tong University, Shanghai, China

    Bin Sheng

  2. Shanghai Jiao Tong University, Shanghai, China

    Lei Bi

  3. University of Sydney, Sydney, NSW, Australia

    Jinman Kim

  4. MIRALab-CUI, University of Geneve, Carouge, Geneve, Switzerland

    Nadia Magnenat-Thalmann

  5. Swiss federal Institute of Technology, Lausanne, Switzerland

    Daniel Thalmann

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Chen, Y., Zhou, Y., Dong, T. (2024). Cross-Modal Information Aggregation and Distribution Method for Crowd Counting. In: Sheng, B., Bi, L., Kim, J., Magnenat-Thalmann, N., Thalmann, D. (eds) Advances in Computer Graphics. CGI 2023. Lecture Notes in Computer Science, vol 14498. Springer, Cham. https://doi.org/10.1007/978-3-031-50078-7_9

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  • DOI: https://doi.org/10.1007/978-3-031-50078-7_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-50077-0

  • Online ISBN: 978-3-031-50078-7

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