Abnormal behavior detection method and system

Abstract

The invention discloses an abnormal behavior detection method and system, which integrate the architecture advantages of an edge server and a cloud on the basis of using a deep learning technology, and ensure high response and low delay of the edge server. And the edge server uploads the abnormal behavior appearing in the open scene to the cloud and downloads the newly added behavior category in the cloud system. And the cloud represents the abnormal behavior by the known behavior through active label learning. Therefore, a perfect abnormal behavior detection closed loop of the edge server and the cloud is constructed, the edge server does not need to keep long connection with the cloud, the network transmission pressure is reduced, and the behavior category support update of the abnormal behavior better meets the abnormal behavior detection requirement in an open scene.

Description

Technical field [0001] The present invention relates to the field of deep learning, and is specifically an abnormal behavior detection method and system. Background technique [0002] The rapid development of smart cities, the large-scale deployment of the monitoring equipment and 5G high-speed network, causing a large number of security video data to grow, more seriously, such monitoring video data also contains abnormal behavior data, seriously harms the safety of the city. Therefore, how to effectively manage, analyze and excavate the abnormal behavior of public places has become one of the most concerned issues in the industry. [1] . In the past, video surveillance systems rely on a large number of artificial participation, and due to the cause of staff fatigue, it is easy to cause missed inspection, misunderstanding, and cannot guarantee real-time dynamic analysis of abnormal behavior detection, so that automation analysis of monitoring video content is one Urgent needs. Wit...

AI Technical Summary

Problems solved by technology

Moreover, the supervised learning process of this type of system inputs a complete video of a complete behavior category, but because certain behaviors often appear repeatedly in the video, the coarse-grained input leads to the final prediction of the behavior category is still not accurate enough [8] , the recognition results can only roughly describe the abnormal type, and cannot show the reasons for the abnormal behavior, including the time and type of behavior, etc. [9][10]

[0005] Specifically, the abnormal behavior detection system [11] Although it is an advanced technology combining the edge server and the cloud, the system has the following technical defects: 1) The predicted behavior category and cycle consistency points of the input video sequence obtained by the system are not used as the input of the convolutional layer, resulting in low accuracy of abnormal behavior recognition; 2) During the operation of the system, it completely relies on the automatic adjustment inside the system, and no external manual intervention parameters are added, which further leads to a low accuracy rate of abnormal behavior recognition; 3) What the system cloud finally provides to the edge server is active learning and training. Abnormal behavior detection model, the edge server needs to replace the previous abnormal behavior detection model, poor operation convenience

[0006] To sum up, on the one hand, the existing abnormal behavior detection cannot meet the needs of abnormal behavior detection in open scenarios due to network transmission delay and supervised learning based on a preset number of behavior category data.

On the other hand, the abnormal behavior detection system [11] Although the edge server, cloud and active learning process are used at the same time, due to a large number of technical defects, it is not effective in identifying abnormal behaviors and operating convenience in open scenarios.

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