Video-based non-supervision abnormal event real-time detection method

Abstract

The invention provides a video-based non-supervision abnormal event real-time detection method. Specific to the aims of reducing the quantity of inter-frame feature points and lowering the complexity in calculation of the feature points at the same time, the feature points are detected with an interval method, namely, a video is segmented, the feature points are detected on a first frame, and only tracking is required subsequently. The calculation amount of a tracking method is small relatively, so that the calculation complexity is lowered greatly. At the end of one segment of video, the feature points are detected once again. After the feature points of each video segment are obtained, direction, speed and location histograms of motion feature points are extracted and connected in series to serve as features of the video segments. Then, the features are subjected to Gaussian mixture modeling and updated in real time to obtain the probability of abnormal events in order to judge whether any abnormal event occurs or not. Thus, the abnormal events can be detected in real time.

Description

technical field [0001] The invention relates to video detection technology. Background technique [0002] With the development of social economy, people's demand for safety is also rising. Abnormal event detection, as an important part of the security monitoring system, has received widespread attention. The performance of the video monitoring system and the effectiveness of monitoring directly affect the overall effect of the security system. Abnormal event detection detects suspicious events in video files and automatically triggers alarms through automatic analysis and research on surveillance image files, and should be able to predict future events with a certain degree of reliability based on specific events in current video surveillance files Analysis verdict. The earliest video surveillance was mainly used for criminal investigation, and it was generally played back after the case occurred, requiring manual search for the situation when the incident occurred. Becau...

AI Technical Summary

Problems solved by technology

Since this method uses multi-scale optical flow motion histograms as features, the feature extraction is more complicated, and it is difficult to meet the real-time requirements.

When modeling abnormal events, the sparse reconstruction method or clustering method is used, and the threshold needs to be manually set, which is difficult to apply to many occasions

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