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Abnormal Behavior Detection Method in Video Based on Key Region Feature Learning

A key area and feature learning technology, applied in the field of computer vision, can solve the problems of complex and diverse behavior patterns, difficult to establish effective models, etc., to achieve the effect of improving detection performance and stability

Active Publication Date: 2020-06-26
UNIV OF SCI & TECH OF CHINA
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Problems solved by technology

The main defect of this type of method is that the behavior patterns are complex and diverse, and it is difficult to establish an effective model

Method used

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  • Abnormal Behavior Detection Method in Video Based on Key Region Feature Learning
  • Abnormal Behavior Detection Method in Video Based on Key Region Feature Learning
  • Abnormal Behavior Detection Method in Video Based on Key Region Feature Learning

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Embodiment Construction

[0046] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0047] Embodiments of the present invention provide a method for detecting abnormal behavior in videos based on key region feature learning, such as figure 1 As shown, firstly, the video frame sequence is divided into several spatio-temporal video blocks, and the spatio-temporal video blocks that may appear abnormal are detected through the key area detection algorithm; then, the trained feature extraction module based on the self-encoder is used to...

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Abstract

The invention discloses a method for detecting abnormal behavior in video based on key region feature learning, comprising: dividing a video frame sequence into several spatio-temporal video blocks, and detecting possible abnormal spatio-temporal video blocks through a key region detection algorithm; Use the trained feature extraction module based on the self-encoder to extract the features of each possible abnormal spatio-temporal video block; use the Mahalanobis distance-based classifier to judge whether the corresponding spatio-temporal video block is abnormal based on the extracted features. This method can automatically detect the scene of abnormal behavior in the video, and has the advantages of fast detection speed and high stability.

Description

technical field [0001] The invention relates to the technical field of computer vision, in particular to a method for detecting abnormal behaviors in videos based on key region feature learning. Background technique [0002] Abnormal behavior detection in video is an important problem in the field of computer vision. The video information of the target area is captured by the camera, and the algorithm needs to automatically detect the behavior that does not conform to the conventional pattern in the video (violation of traffic rules, fighting, illegal theft, etc.). [0003] Existing methods are mainly divided into three types: [0004] (1) The method based on trajectory analysis. Use the target tracking algorithm to obtain the trajectory map of the moving target in the video, and then use a specific trajectory analysis method to analyze whether a certain trajectory belongs to an abnormal trajectory. This type of method can only analyze abnormalities such as speed and direct...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/00G06K9/46G06K9/62
CPCG06V20/46G06V20/41G06V10/462G06F18/2413G06F18/22
Inventor 杨文飞刘斌俞能海
Owner UNIV OF SCI & TECH OF CHINA
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