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Panoramic Human Activity Recognition

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Computer Vision – ECCV 2022 (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13664))

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Abstract

To obtain a more comprehensive activity understanding for a crowded scene, in this paper, we propose a new problem of panoramic human activity recognition (PAR), which aims to simultaneously achieve the recognition of individual actions, social group activities, and global activities. This is a challenging yet practical problem in real-world applications. To track this problem, we develop a novel hierarchical graph neural network to progressively represent and model the multi-granular human activities and mutual social relations for a crowd of people. We further build a benchmark to evaluate the proposed method and other related methods. Experimental results verify the rationality of the proposed PAR problem, the effectiveness of our method and the usefulness of the benchmark. We have released the source code and benchmark to the public for promoting the study on this problem.

H. Yan and J. Li—Equal Contribution.

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Acknowledgment

This work was supported by the National Natural Science Foundation of China under Grants U1803264, 62072334, and the Tianjin Research Innovation Project for Postgraduate Students under Grant 2021YJSB174.

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Authors and Affiliations

  1. Intelligence and Computing College, Tianjin University, Tianjin, China

    Ruize Han, Haomin Yan, Jiacheng Li, Songmiao Wang & Wei Feng

  2. University of South Carolina, Columbia, USA

    Song Wang

Authors
  1. Ruize Han
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  2. Haomin Yan
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  3. Jiacheng Li
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  4. Songmiao Wang
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  5. Wei Feng
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  6. Song Wang
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Corresponding authors

Correspondence to Ruize Han , Wei Feng or Song Wang .

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Editors and Affiliations

  1. Tel Aviv University, Tel Aviv, Israel

    Shai Avidan

  2. University College London, London, UK

    Gabriel Brostow

  3. Google AI, Accra, Ghana

    Moustapha Cissé

  4. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Facebook (United States), Menlo Park, CA, USA

    Tal Hassner

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Han, R., Yan, H., Li, J., Wang, S., Feng, W., Wang, S. (2022). Panoramic Human Activity Recognition. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13664. Springer, Cham. https://doi.org/10.1007/978-3-031-19772-7_15

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  • DOI: https://doi.org/10.1007/978-3-031-19772-7_15

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