Method for distinguishing abnormal conditions of monitoring camera on basis of background comparison

Abstract

The invention discloses a method for distinguishing the abnormal conditions of a monitoring camera on the basis of background comparison. The method comprises the following steps of (1) conducting background extraction, wherein a background image in a monitoring video scene is extracted; (2) conducting comparison and analysis, wherein each frame of picture in a monitoring video is compared with the background image through a computer image visual analysis technology; (3) conducting abnormal condition judgment, wherein when the difference value between one frame of image and the background image is larger than a set value, it is judged that abnormal conditions exist. The method for distinguishing the abnormal conditions of the monitoring camera on the basis of background comparison has the advantages that the dependence of video monitoring work on the personnel can be reduced, the situation that abnormal conditions cannot be found in time due to the fact that the personnel feel tired, and therefore the launching quality of the video monitoring work is improved.

Description

technical field [0001] The invention relates to the technical field of video processing, in particular to a method for distinguishing abnormal situations of surveillance cameras based on background comparison. Background technique [0002] When watching surveillance video manually, it often causes visual fatigue due to too long time. At this time, if the camera is artificially blocked or moved, or the signal is lost due to line failure, it is often not discovered by the surveillance personnel in time, thus affecting the video. Monitor the quality of work carried out. [0003] So far, there is no public information to provide effective technical solutions for the above problems. [0004] In view of the above-mentioned problems, the present invention provides a method for discriminating abnormalities of surveillance cameras based on background comparison, which can detect abnormalities such as camera movement, occlusion, and signal interruption in time. . Contents of the i...

AI Technical Summary

Problems solved by technology

[0002] When watching surveillance video manually, it often causes visual fatigue due to too long time. At this time, if the camera is artificially blocked or moved, or the signal is lost due to line failure, it is often not discovered by the surveillance personnel in time, thus affecting the video. Monitor the quality of work carried out

[0003] As of now, there is no public information to provide effective technical solutions for the above problems

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