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Analysis method of video-based crowd density and abnormal behavior detection system

A crowd density and detection system technology, applied in the direction of instruments, character and pattern recognition, computer components, etc., can solve the problems of slow processing speed and unfavorable application, and achieve the effect of high real-time performance and accurate detection

Active Publication Date: 2017-11-07
SICHUAN UNIV
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Problems solved by technology

Liu et al. proposed to use a series of models based on the movement of the main body to discover the trajectory of the group movement, so as to identify the movement of the group. This method has a good recognition effect, but the processing speed is too slow, which is not conducive to practical application.

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  • Analysis method of video-based crowd density and abnormal behavior detection system
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  • Analysis method of video-based crowd density and abnormal behavior detection system

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Embodiment Construction

[0060] The present invention will be further described below in conjunction with accompanying drawing:

[0061] Such as figure 1 As shown, the process in the figure is a brief description of the main process of the following method, and the following steps are used to analyze the video image for monitoring:

[0062] (1) Crowd density classification:

[0063] Combining pixel point statistics and texture features, the pixel point statistical method is used to pre-estimate the crowd. When it is judged that the current crowd density is too high, the texture feature is used to estimate the crowd density. This method not only uses the pixel point statistical method to achieve The advantages of simplicity and speed, and the texture feature method can be used for further density classification in high-density crowds.

[0064] Crowd density classification based on pixel statistics:

[0065] (a) First convert the image to be processed into a grayscale image, and use the Canny edge de...

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Abstract

The invention discloses an analysis method of a video-based crowd density and abnormal behavior detection system, and relates to the fields such as intelligent video surveillance and target detection. The analysis method comprises the steps of performing density grading by using an improved method based on pixel statistics and texture statistics; performing estimation on the number of people by using a method combining pixel statistics and foreground corner detection; introducing a local optical flow, proposing and implementing a crowd abnormal behavior detection algorithm based on the average kinetic energy change rate. The analysis method disclosed by the invention not only can be applicable to general surveillance video, but also can be more applicable to crowd gathering video in prominent large public places. The analysis method is less in system time consumption, has good effectiveness and practicability and meets actual requirements.

Description

technical field [0001] The present invention relates to an analysis method of a video-based crowd density and abnormal behavior detection system in the field of public security, in particular to an improved crowd density classification method based on the combination of pixel point statistics and texture statistics, based on pixel point statistics and corner point detection Combined with the number estimation method, a crowd abnormal behavior detection algorithm based on the average kinetic energy change rate is proposed and implemented, which belongs to the field of machine vision and intelligent information processing. Background technique [0002] In recent years, with the rapid development of the economy and the continuous increase of people's social activities, the phenomenon of crowd congestion in public places such as transportation hubs, large-scale event sites, and large shopping malls has become more and more frequent. serious. Therefore, in order to maintain publ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00
CPCG06V20/41G06V20/53
Inventor 何小海韦招静吴晓红卿粼波熊杰滕奇志王正勇
Owner SICHUAN UNIV
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