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Human Interaction Learning on 3D Skeleton Point Clouds for Video Violence Recognition

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Book cover Computer Vision – ECCV 2020 (ECCV 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12349))

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Abstract

This paper introduces a new method for recognizing violent behavior by learning contextual relationships between related people from human skeleton points. Unlike previous work, we first formulate 3D skeleton point clouds from human skeleton sequences extracted from videos and then perform interaction learning on these 3D skeleton point clouds. A novel Skeleton Points Interaction Learning (SPIL) module, is proposed to model the interactions between skeleton points. Specifically, by constructing a specific weight distribution strategy between local regional points, SPIL aims to selectively focus on the most relevant parts of them based on their features and spatial-temporal position information. In order to capture diverse types of relation information, a multi-head mechanism is designed to aggregate different features from independent heads to jointly handle different types of relationships between points. Experimental results show that our model outperforms the existing networks and achieves new state-of-the-art performance on video violence datasets.

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Acknowledgment

This work was supported by NSFC 61876208, Key-Area Research and Development Program of Guangdong 2018B010108002, National Research Foundation Singapore under its AI Singapore Programme (AISG-RP-2018-003) and the MOE Tier-1 research grants: RG28/18 (S) and RG22/19 (S).

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Authors and Affiliations

  1. School of Software Engineering, South China University of Technology, Guangzhou, China

    Yukun Su, Jinhui Zhu & Qingyao Wu

  2. Key Laboratory of Big Data and Intelligent Robot, Ministry of Education, Beijing, China

    Yukun Su, Jinhui Zhu & Qingyao Wu

  3. Nanyang Technological University, Singapore, Singapore

    Guosheng Lin

Authors
  1. Yukun Su
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  2. Guosheng Lin
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  3. Jinhui Zhu
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  4. Qingyao Wu
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Corresponding authors

Correspondence to Guosheng Lin or Qingyao Wu .

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Editors and Affiliations

  1. University of Oxford, Oxford, UK

    Andrea Vedaldi

  2. Graz University of Technology, Graz, Austria

    Horst Bischof

  3. University of Freiburg, Freiburg im Breisgau, Germany

    Thomas Brox

  4. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Jan-Michael Frahm

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Su, Y., Lin, G., Zhu, J., Wu, Q. (2020). Human Interaction Learning on 3D Skeleton Point Clouds for Video Violence Recognition. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12349. Springer, Cham. https://doi.org/10.1007/978-3-030-58548-8_5

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  • DOI: https://doi.org/10.1007/978-3-030-58548-8_5

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