Detection method based on group environment abnormal behavior

Abstract

The invention belongs to a detection method based on a group environment abnormal behavior in the technical field of computer motion image identification and monitoring, comprising the following steps of: dividing video unit subsequences in the establishment of a detection model, extracting characteristics, establishing a sample database and establishing an Multi-HMM model; extracting the sequence of each observed value from a video sequence of the current monitoring scene in abnormal behavior detection, confirming the optimal hidden Markov chains corresponding to the sequences of the observed values, and judging and warming abnormal behaviors. By accurately and rapidly extracting the dynamic changing characteristic of a video sequence on the frequency domain along with the change of time based on the whole angle and automatically detecting abnormal behaviors under group environments in real time according to the established model, the invention achieves the accuracy rate of about 90 percent, thereby having the characteristics of accurately and rapidly extracting the behavior characteristic of the current monitored scene, being widely used for detecting the abnormal behaviors happened under the group environments, having high detection efficiency, accuracy and reliability, and the like.

Description

technical field [0001] The invention belongs to the technical field of pattern recognition and monitoring processing of computer moving images, in particular to a method of feature extraction using high-frequency spatio-temporal features, Multi-HMM (Multi-chain Hidden Markov Model) abnormal behavior detection, etc. A method for detecting abnormal behavior. Background technique [0002] The monitoring scene in the group environment usually contains more pedestrians. If abnormal events occur in the monitoring scene, such as fights, gang fights, riots, etc., it will not only endanger the safety of public property and personal safety, but also may intensify the problem and bring harm to the harmonious development of society. Negative impact. Therefore, the detection of group abnormal events is closely related to personal safety and is related to the healthy and harmonious development of society. At present, abnormal behavior detection methods mainly focus on the analysis of pe...

AI Technical Summary

Problems solved by technology

This method uses the optical flow method to extract local motion features, but the optical flow method is easily affected by the light. In the area where the pixels are very close, the more accurate optical flow is usually not detected.

Therefore, this method has defects such as prone to misjudgment, poor reliability, and low scalability of the system.

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