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

An anomaly detection and dual-modal technology, applied in the field of computer vision, can solve problems such as labeling

Active Publication Date: 2022-04-26
SHANGHAI JIAOTONG UNIV
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  • Description
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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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  • Dual-modal iterative denoising anomaly detection method and terminal based on video weak markers
  • Dual-modal iterative denoising anomaly detection method and terminal based on video weak markers
  • Dual-modal iterative denoising anomaly detection method and terminal based on video weak markers

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Embodiment Construction

[0056] Embodiments of the present invention are described below through specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific implementation modes, and various modifications or changes can be made to the details in this specification based on different viewpoints and applications without departing from the spirit of the present invention.

[0057] The present invention adopts the multi-instance learning method as the main frame of anomaly detection, divides the video into multiple video clips as input units, greatly expands the amount of data that can be used by the training network framework in terms of dimensions, and achieves good results in the end. Basic guarantees are provided. The invention uses a multi-instance learning framework for preliminary denoising, uses a...

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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...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06V20/40G06V10/30G06V10/764G06V10/774G06K9/62
CPCG06V20/40G06V10/30G06F18/2415G06F18/214
Inventor 杨华林书恒
Owner SHANGHAI JIAOTONG UNIV
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