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Medium scale crowd abnormal behavior detection method based on causal network analysis

A causal network and detection method technology, applied in character and pattern recognition, instruments, computer components, etc., can solve the problems of loose motion patterns, unfeasible and unapplied recognition of individual postures, etc.

Inactive Publication Date: 2013-04-03
YANSHAN UNIV
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Problems solved by technology

The limitations of the above methods are: the macro-analysis method ignores the characteristics of the individual's position and movement direction, and cannot be applied to occasions where pedestrian movement has no common physical laws; the micro-analysis method cannot be applied to crowds with a large number of people and mutual occlusion , it is not feasible to recognize the pose of the individual
[0004] Common crowds in real life usually have a medium size and loose motion patterns. The direction and speed of pedestrians' movement are relatively free, so macroscopic methods cannot be used to identify them. At the same time, pedestrians occlude each other seriously, making it difficult to identify individual postures

Method used

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  • Medium scale crowd abnormal behavior detection method based on causal network analysis
  • Medium scale crowd abnormal behavior detection method based on causal network analysis
  • Medium scale crowd abnormal behavior detection method based on causal network analysis

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[0074] Example: Build a causal cognitive network to analyze two behaviors of chatting and wandering, as shown in the attached image 3 As shown, the network parameters of the two behaviors are significantly different, such as the maximum value of betweenness, betweenness reflects the force and influence of a target in the entire network, wandering can be regarded as an abnormal behavior, in this behavior There is a target who is the central figure of the crowd, and the central figure often has a greater force and influence, so the betweenness value of this target is the largest, and in the behavior of chatting, the role difference between individuals is not large, so the betweenness The maximum value of the number is obviously smaller than that of wandering behavior. Therefore, the use of network parameters can effectively identify crowd behavior.

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Abstract

The invention discloses a medium scale crowd abnormal behavior detection method based on causal network analysis. The interaction among individuals is expressed by using a cognitive concept and social force model, the association among individuals is estimated according to Granger test, the causal cognitive complex network is constructed according to the communication among individuals, and the crowd behavior is expressed and analyzed on the macro by analyzing the function parameter of the network while the individual microcosmic information is remained, thus the abnormal behavior of the medium scale crowd can be detected and judged effectively.

Description

technical field [0001] The invention relates to a method for detecting abnormal behaviors of crowds in the field of video analysis and image understanding, in particular to a method for detecting abnormal behaviors of medium-sized crowds based on causal network analysis. Background technique [0002] Crowd abnormal behavior detection is a cutting-edge topic and research difficulty in the field of video surveillance and image analysis. It has urgent needs in the fields of public place monitoring (such as airports, subway stations, campuses, etc.), security management, etc., such as crime alarms, multi-channel video screening , event retrieval in long videos, etc. [0003] The method of crowd behavior analysis depends on the scale and operation mode of the crowd: for a large crowd with a common movement pattern, it is often regarded as a whole, and its macroscopic characteristics are analyzed from the overall external performance of the crowd; For scenes with a large number o...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62
Inventor 张旭光刘春霞
Owner YANSHAN UNIV
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