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Holistic Crowd Interaction Modelling for Anomaly Detection

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Biometric Recognition (CCBR 2017)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10568))

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Abstract

Dense crowd motion analysis in surveillance scenario is a daunting task that when occlusion and low resolution happen, it is difficult to make effective use of pedestrian detection and tracking algorithms. In this study, we introduce a crowd interaction modelling framework inspired by physical and social science studies. Instead of taking the pedestrian individual as the unit of analysis, the interaction among individuals could be modeled through the social force model (SFM), and for robust representation, a modified SFM is proposed. Experiments of the visualization and anomaly detection tested on UMN and Web dataset indicate SFM-based interaction modelling outperform optical flow and particle advection.

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Acknowledgments

This work is supported by the National Key R&D Program of China under Grant 2017YFB0802300, the National Natural Science Foundation of China 61601223, Natural Science Foundation of Jiangsu Province BK20150756, and Post-doctoral Science Foundation of China 2015M580427.

Author information

Authors and Affiliations

  1. Collaborative Innovation Center of Novel Software Technology and Industrialization, College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, People’s Republic of China

    Jiaxing Pan & Dong Liang

Authors
  1. Jiaxing Pan
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  2. Dong Liang
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Corresponding author

Correspondence to Jiaxing Pan .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Jie Zhou

  2. Beihang University, Beijing, China

    Yunhong Wang

  3. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Zhenan Sun

  4. Computing and Technology, Chinese Academy of Sciences, Beijing, China

    Yong Xu

  5. Shenzhen University, Shenzhen, China

    Linlin Shen

  6. Tsinghua University, Beijing, China

    Jianjiang Feng

  7. Chinese Academy of Sciences, Beijing, China

    Shiguang Shan

  8. Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

    Yu Qiao

  9. Graduate School at Shenzhen, Tsinghua University, Shenzhen, China

    Zhenhua Guo

  10. Shenzhen University, Shenzhen, China

    Shiqi Yu

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Pan, J., Liang, D. (2017). Holistic Crowd Interaction Modelling for Anomaly Detection. In: Zhou, J., et al. Biometric Recognition. CCBR 2017. Lecture Notes in Computer Science(), vol 10568. Springer, Cham. https://doi.org/10.1007/978-3-319-69923-3_69

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  • DOI: https://doi.org/10.1007/978-3-319-69923-3_69

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-69922-6

  • Online ISBN: 978-3-319-69923-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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