Congestion detection of pedestrians using the velocity entropy: A case study of Love Parade 2010 disaster
Congestion detection of pedestrians using the velocity entropy: A case study of Love Parade 2010 disaster
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DOI：
10.1016/j.physa.2015.08.013
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发表时间：
2015-12-15
影响因子：
3.3
通讯作者：
Yuan, Hongyong
中科院分区：
文献类型：
--
作者：
Huang, Lida;Chen, Tao;Yuan, Hongyong
大量人群的聚集经常导致人群灾难，比如2010年7月24日在德国杜伊斯堡发生的“爱情游行灾难”。为了避免这些悲剧的发生，视频监控和预警变得越来越重要。本文首先将速度熵定义为拥塞检测的判据，它同时表示网络的运动幅度分布和运动方向分布。然后利用AnyLogic软件的仿真数据对检测方法进行验证。为了测试该方法的泛化性能，实验中还使用了一个真实案例的视频记录，即爱情游行灾难。通过高斯混合模型和光流计算提取视频中前景物体的速度直方图。利用时序变点检测算法，将速度熵应用于爱心游行庆典的拥堵检测。事实证明，我们的方法可以在不识别和跟踪单个行人的情况下实时检测人群的异常行为。(C) 2015 Elsevier B.V.版权所有
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. (C) 2015 Elsevier B.V. All rights reserved.