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Congestion detection of pedestrians using the velocity entropy: A case study of Love Parade 2010 disaster

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  • Huang, Lida
  • Chen, Tao
  • Wang, Yan
  • Yuan, Hongyong

Abstract

Gatherings of large human crowds often result in crowd disasters such as the Love Parade Disaster in Duisburg, Germany on July 24, 2010. To avoid these tragedies, video surveillance and early warning are becoming more and more significant. In this paper, the velocity entropy is first defined as the criterion for congestion detection, which represents the motion magnitude distribution and the motion direction distribution simultaneously. Then the detection method is verified by the simulation data based on AnyLogic software. To test the generalization performance of this method, video recordings of a real-world case, the Love Parade disaster, are also used in the experiments. The velocity histograms of the foreground object in the videos are extracted by the Gaussian Mixture Model (GMM) and optical flow computation. With a sequential change-point detection algorithm, the velocity entropy can be applied to detect congestions of the Love Parade festival. It turned out that without recognizing and tracking individual pedestrian, our method can detect abnormal crowd behaviors in real-time.

Suggested Citation

  • Huang, Lida & Chen, Tao & Wang, Yan & Yuan, Hongyong, 2015. "Congestion detection of pedestrians using the velocity entropy: A case study of Love Parade 2010 disaster," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 440(C), pages 200-209.
    • Handle: RePEc:eee:phsmap:v:440:y:2015:i:c:p:200-209
    DOI: 10.1016/j.physa.2015.08.013
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    References listed on IDEAS

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    1. Anders Johansson & Dirk Helbing & Habib Z. Al-Abideen & Salim Al-Bosta, 2008. "From Crowd Dynamics To Crowd Safety: A Video-Based Analysis," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 11(04), pages 497-527.
    2. Zhang, X.L. & Weng, W.G. & Yuan, H.Y. & Chen, J.G., 2013. "Empirical study of a unidirectional dense crowd during a real mass event," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(12), pages 2781-2791.
    3. Armin Seyfried & Oliver Passon & Bernhard Steffen & Maik Boltes & Tobias Rupprecht & Wolfram Klingsch, 2009. "New Insights into Pedestrian Flow Through Bottlenecks," Transportation Science, INFORMS, vol. 43(3), pages 395-406, August.
    4. Dirk Helbing & Pratik Mukerji, "undated". "Crowd Disasters as Systemic Failures: Analysis of the Love Parade Disaster," Working Papers ETH-RC-12-010, ETH Zurich, Chair of Systems Design.
    5. Liu, Xuan & Song, Weiguo & Zhang, Jun, 2009. "Extraction and quantitative analysis of microscopic evacuation characteristics based on digital image processing," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(13), pages 2717-2726.
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    Cited by:

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