Video anomaly detection method and system based on generation of collaborative discrimination network

Abstract

The invention relates to a video anomaly detection method and system based on generation of a collaborative discrimination network. The method comprises the steps: collecting normal video monitoring data, converting the normal video monitoring data into an original frame, selecting an original future frame, and adding noise into the original future frame to obtain a noise future frame; inputting the original frame into a generator to obtain a predicted future frame; calculating optical flow information between the predicted future frame and a previous frame of the original future frame; calculating the optical flow information between the original future frame and the previous frame of the original future frame and the difference between the two frames, inputting the predicted future frame and the original future frame into a discriminator, and constructing a target function of the discriminator; inputting the noise future frame and the predicted future frame into a coordinator, and constructing a target function of the coordinator; constructing a target function of the generator; updating the generator, and determining a predicted future frame during testing; and calculating an abnormal score, and determining whether a to-be-detected frame is abnormal or not according to the abnormal score. According to the method, the detection precision of the anomaly in videos can be improved.

Description

technical field [0001] The invention relates to the field of computer vision, in particular to a video anomaly detection method and system based on a generated collaborative discriminant network. Background technique [0002] With the continuous development of social economy, science and technology, social security issues are becoming more and more complicated, and public security has become the focus of great attention. Video surveillance has been widely used in daily life and plays an important role in ensuring public safety. However, ordinary video surveillance can only perform shooting and recording functions. If abnormal events such as fights and violations of public order occur, the surveillance system cannot perform detection functions, and it will cost a lot of money to hire surveillance personnel. At the same time, with the continuous increase in the number of surveillance cameras, it is difficult for surveillance personnel to observe abnormalities in all surveilla...

AI Technical Summary

Problems solved by technology

At the same time, the video collected in the real scene may be disturbed by noise, which will affect the accuracy of anomaly detection

At the same time, due to the limitations of external scenes or video sensors, interference becomes an inevitable problem

Therefore, the performance of anomaly detection may be affected by noise interference in the collected videos

Moreover, most current algorithms pay attention to the performance of the network but ignore the impact of noise on performance.

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