Dense passenger flow abnormal behavior detection method

Abstract

The invention provides a dense passenger flow abnormal behavior detection method. The method mainly involves extracting and analyzing group motion features in a vision scope of each pick-up head in a sparse monitoring network, respectively determining motion behavior outside the vision scope of each pick-up head and an orientation in which abnormal motion behavior may take place and determining behavior of group motion of a monitoring blind area composed of areas outside the vision scope of each pick-up head accordingly. The method realizes detection and determining of the behavior of group abnormal motion outside a monitoring vision field through analyzing the group motion behavior in the monitoring vision field of the sparse monitoring network and realizes formation of a monitoring network capable of covering large areas through a small number of pick-up heads.

Description

technical field [0001] The invention relates to the technical field of video image information processing, in particular to a method for detecting abnormal behavior of dense passenger flow. Background technique [0002] In recent years, there have been many research results and practical applications of video surveillance technology in the direction of intelligence and networking. With the continuous improvement of public security needs, on the one hand, the behavior detection and analysis of only a few individuals can no longer meet the monitoring needs, and group movement analysis has attracted more and more attention. On the other hand, in order to realize the seamless monitoring of public places, it is necessary to deploy a large number of video monitoring points to expand the monitoring range. The ideal mode is to use multiple cameras to fully cover the monitoring area. These cameras overlap or are adjacent to each other in the field of view to obtain a continuous scen...

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Problems solved by technology

However, this will leave monitoring blind spots between different camera fields of view. Once abnormalities occur in these areas, managers will not be able to detect them in time and deal with them accordingly.

[0003] Therefore, it is necessary to propose a method for detecting abnormal behaviors of dense passenger flow, so as to effectively solve the problem that the monitoring blind spots in the sparse monitoring network cannot be detected in time and dealt with accordingly

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