A monitoring video abnormity detection method based on unsupervised learning

Abstract

The invention provides a monitoring video abnormity detection method based on unsupervised learning. According to the method, firstly, a motion block in a video is extracted, then abnormity detectionis carried out from two different angles of local and global, and a detection result is more accurate through diversified detection angles. In local anomaly detection, firstly, a motion block in a video is expanded, then the expanded motion block is used as a basic detection unit, and the difference between the motion block and a neighborhood motion block of the motion block is compared from the time dimension, the space dimension and the space-time dimension; In global anomaly detection, firstly, moving blocks in a video are clustered to extract moving targets, then a sliding window is used on a moving target sequence, the difference between the two moving targets in the window is compared, and finally, a detection result is optimized based on the consistency. The method is suitable for abnormal detection of the monitoring video, low in calculation complexity, accurate in detection result and good in robustness. The method has wide application in the technical field of video analysis.

Description

technical field [0001] The invention belongs to the technical field of video analysis, and relates to a method for detecting abnormal objects and motion patterns in a monitoring video, in particular to a method for detecting abnormalities in a monitoring video based on unsupervised learning. Background technique [0002] Anomaly detection of surveillance video is an important research field in computer vision. It can intelligently analyze surveillance video. Compared with manual analysis, it greatly improves detection efficiency and accuracy, and saves a lot of manpower and material resources. At present, surveillance video anomaly detection technology has been widely used in traffic violation detection, subway fare evasion detection, fire warning, intrusion monitoring, etc. [0003] At present, most surveillance video anomaly detection algorithms are based on supervised learning and semi-supervised learning. The anomaly detection method based on supervised learning needs t...

AI Technical Summary

Problems solved by technology

At present, although there are a few anomaly detection methods based on unsupervised learning, these methods have a single detection angle and cannot guarantee the detection accuracy; most of them only consider the difference between the detection target and other targets in its spatial neighborhood, ignoring the Time and space-time information cannot guarantee the detection accuracy; only the threshold method is used to judge the abnormality of the detection results. If the threshold is not selected properly, it may cause false detection

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