Dual-modal iterative denoising anomaly detection method and terminal based on video weak markers

Abstract

The present invention provides a dual-mode iterative denoising anomaly detection method and terminal based on video weak labels, which regard weak labels as the noise of accurate labels, and perform label denoising from image control and feature space respectively; Image space to learn the characteristics of normal and abnormal videos; use graph convolutional models to learn the characteristics of video clips at different times; use iteration to update classifiers and denoisers alternately. The present invention overcomes the difficulty of labeling data by fully considering the problem of weak labeling of video, and uses the method of denoising model. Facing the research field of abnormal detection, which is difficult to collect data, it has strong robustness and can solve the problem of video very well. Weak labeling problem with general applicability.

Description

technical field [0001] The invention belongs to the technical field of computer vision, specifically a dual-mode iterative denoising abnormality detection method and terminal based on video weak marks, and particularly relates to the abnormality detection focusing on crowd abnormal behavior under a surveillance camera. Background technique [0002] Surveillance cameras are increasingly being used in public places, such as streets, intersections, banks and shopping malls, where there is a lot of traffic. However, the relevant administrative law enforcement agency's ability to detect abnormalities in the surveillance video has not kept up, resulting in the inability to fully utilize the resources of the surveillance camera, and there are obvious defects in its use. It is also very unrealistic for people to observe the surveillance video in real time, because the number of surveillance cameras in our country is already very large. It is not only costly to rely on human resource...

AI Technical Summary

Problems solved by technology

[0007] Aiming at the weak label problem of the current data set, the present invention regards the weak label as the noise of the precise label, and denoises the label from the image control and feature space respectively; uses the self-encoder to learn the characteristics of normal and abnormal videos from the image space; uses the graph The convolutional model learns the features of video clips at different times; iteratively updates the classifier and denoiser alternately

The present invention overcomes the difficulty of labeling data by fully considering the problem of weak labeling of video, and uses the method of denoising model. Facing the research field of abnormal detection, which is difficult to collect data, it has strong robustness and can solve the problem of video very well. If labeling is a problem, it has general applicability

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