Group abnormal behavior detection method based on air monitoring platform

A technology for monitoring platforms and detection methods, applied in the fields of instruments, character and pattern recognition, computer parts, etc.

Active Publication Date: 2018-09-28
SICHUAN UNIV
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  • Application Information

AI Technical Summary

Problems solved by technology

The trajectory-based method tracks the moving target to obtain the trajectory of the target, and then distinguishes the normal target from the abnormal target by comparing the similarity of each trajectory. Complex scenes with dense crowds

Method used

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  • Group abnormal behavior detection method based on air monitoring platform
  • Group abnormal behavior detection method based on air monitoring platform
  • Group abnormal behavior detection method based on air monitoring platform

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Embodiment Construction

[0015] The present invention will be further described below in conjunction with accompanying drawing:

[0016] The specific method of feature point extraction and correction of its optical flow vector is as follows:

[0017] In this paper, the Shi-Tomasi algorithm is used to extract corner points as feature points, and the optical flow vector of feature points is obtained by Lucas-Kanade optical flow method, and the optical flow vector is appropriately corrected by using the estimated image depth information. The correction method is as follows: For feature points ( x, y), assuming that the original optical flow vector obtained by the Lucas-Kanade optical flow method is I LK =(I x ,I y ) indicates that the maximum depth value in the image is d max , the minimum depth value is d min , the depth value of the feature point (x, y) is d. Let the correction factor be:

[0018]

[0019] In the formula, e is a natural constant. It can be seen from the above formula that the ...

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Abstract

The invention provides a group abnormal behavior detection method based on an air monitoring platform. Firstly, light flow vectors of feature points are appropriately corrected by estimating depth information of an image to reduce a target movement speed estimation error caused by the perspective phenomenon, then the light flow vectors of the feature points are clustered, and target detection under a moving camera is achieved by combining a background movement consistency law. Abnormal behaviors are detected by adopting a double-Gauss mixed model, and model parameters are solved by using an expectation maximization algorithm. Finally, misjudgment is verified by adopting a time queue mechanism, space coordinates of the abnormal feature points are clustered by means of a simplified agglomerative hierarchical clustering algorithm, the isolated abnormal feature points are removed, and abnormal groups are marked. The validity of the method is verified by experiments in multiple scenes.

Description

technical field [0001] The invention relates to the problem of abnormal behavior detection in the field of video intelligent monitoring, in particular to a group abnormal behavior detection method based on an aerial monitoring platform. Background technique [0002] Although abnormal behavior detection algorithms based on fixed cameras have become a research hotspot, there are few researches on abnormal behavior detection algorithms based on aerial surveillance platforms. Different from the video captured by fixed cameras, the video captured by the aerial surveillance platform has the characteristics of background movement and long viewing distance. It is necessary to combine these characteristics to design an abnormal behavior detection method based on the aerial surveillance platform. [0003] Because the background of the video screen captured by the aerial monitoring platform is in a moving state, the classic foreground extraction algorithm is no longer applicable, so th...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62
CPCG06V20/53G06F18/23G06F18/214
Inventor 何小海黄彬卿粼波吴晓红滕奇志王昆仑吴小强
Owner SICHUAN UNIV
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