Abnormal crowd behavior detection algorithm based on optical flow computation

Abstract

The invention relates to an abnormal crowd behavior detection algorithm based on optical flow computation, comprising the following steps: first, extracting the characteristics of crowd movement using an optical flow particulate vector field; then, calculating the interaction force between optical flow particulates based on a social force model; and finally, making a histogram entropy analysis of the interaction force to realize behavior judgment. Through the algorithm, an abnormal crowd behavior can be effectively judged and located. By calculating the interaction force in a social force model corresponding to a scene video and making a histogram entropy analysis, rapid and reliable abnormal crowd behavior detection is realized. The algorithm can be widely used in the field of video monitoring.

Description

technical field [0001] The invention relates to an abnormal crowding behavior detection algorithm based on optical flow calculation, and belongs to the technical field of video monitoring. Background technique [0002] In recent years, there have been many personal injury accidents caused by overcrowded crowds, which have aroused strong concern from the society. In order to prevent such accidents, in addition to strengthening patrol supervision and safety publicity, it is also necessary to technically improve the detection and early warning capabilities of abnormal crowding behavior. [0003] At present, crowd detection for video surveillance is mainly realized by people counting or crowd density estimation. Patent CN200710041086 uses the background difference method to extract the foreground target, uses feature matching to judge the foreground human target, and realizes aggregation detection by counting the number of foreground human targets; patent CN201110329227 first e...

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Problems solved by technology

Patent 201210064543 judges the number of human bodies by extracting SURF feature points in the foreground area, and then clustering the feature points; the document "Crowd aggregation detection method using normalized foreground and two-dimensional joint entropy (Journal of Wuhan University Information Science Edition, 2013.09) "By calculating the two-dimensional joint entropy of the foreground area to count the crowd density in the scene, these two methods solve the occlusion problem to a certain extent, but cannot eliminate the interference of non-human foreground targets

The literature "Crowd Counting in Multiple Crowd Density Scenes (Chinese Journal of Image and Graphics, 2013.04)" uses a regression model to estimate the number of people in a scene, which can estimate the crowd density in a specific scene, but the training process is complicated and the scene adaptability is poor

The literature "Abnormal crowd behavior detection using social force model (CVPR2009)" uses the social force model to identify the abnormal behavior of the crowd, but only uses the modulus of the interaction force in the social force model, which is not comprehensive enough to identify the abnormal behavior of the crowd

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