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

• We propose velocity entropy as an indicator to detect the crowd congestion.
• The velocity entropy denotes the dispersion of velocity distribution on magnitude and directions.
• This method has been applied to the simulation data from AnyLogic and the video recordings of the Love Parade disaster.
• The method is robust and efficient in detecting the congestion.

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.