Method and device of video abnormal behavior detection based on Bayes surprise degree calculation

Abstract

The invention provides a method and a device of video abnormal behavior detection based on Bayes surprise degree calculation. The method comprises the steps of extracting a spatio-temporal interest point (STIP) in a video frame to be used as a point to be detected, and using the movement velocity size and direction of the point to be detected in an optical flow estimation scene as a feature calculation surprise; calculating prior and posterior probability distribution in the spatial dimension and the time dimension by aiming at a video, and respectively calculating a spatial surprise degree and a time surprise degree of each point to be detected; combining a total surprise degree through the time surprise degree and the spatial surprise degree; and warning abnormal situations under the condition that the surprise values of a plurality of points to be detected exceed a threshold value. The device comprises an STIP detection module, a feature extraction module, a surprise calculation module and an anomaly detection module. By means of the method and the device of the video abnormal behavior detection based on the Bayes surprise degree calculation, detection of several specific types of emergent and anomalous events can be achieved, and the abnormal analysis algorithm has good applicability and high classifying accuracy rate.

Description

technical field [0001] The present invention relates to the field of video abnormality analysis and detection, in particular, the present invention relates to a video abnormal behavior detection method and device based on Bayesian surprise degree calculation. Background technique [0002] With the development of information technology, due to the needs of public safety in recent years, the demand for intelligent monitoring has increased rapidly. The existing monitoring needs to rely on special personnel on duty, and the traditional monitoring system cannot give early warning when an incident occurs. It is also extremely time-consuming to select the work that can be used as evidence from a large number of video materials in the later stage. This cannot meet the security requirements of security-sensitive departments such as public security, banks, and transportation for video surveillance. [0003] Abnormal behavior identification is the main task of intelligent monitoring ...

AI Technical Summary

Problems solved by technology

Although these two methods can establish accurate behavior models in fixed scenarios, they need to manually mark a large number of behavior sequences to obtain enough training samples, which will cause a lot of waste of human resources.

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