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.
CN108596045AActive Publication Date: 2018-09-28SICHUAN UNIV
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
SICHUAN UNIV
Publication Date
2018-09-28

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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.
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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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