Video monitoring abnormal behavior detection method and detections system thereof

Abstract

The invention discloses a video monitoring abnormal behavior detection method and a detections system thereof. The method comprises the following steps: obtaining a video image sequence, and establishing a background model by use of a hybrid Gauss model; according to the established background model, extracting a foreground object area image from the video image sequence by use of a background subtraction method, and dividing the foreground object area image into a plurality of foreground object image blocks; calculating a motion label of each foreground object image block by use of the background subtraction method, and extracting a five-dimensional characteristic parameter of each foreground object image block; determining whether a current foreground object image block is a normal behavior image block by use of an offline SVM classifier, and if the current foreground object image block is a normal behavior image block, ending the process; and if the current foreground object image block is an abnormal behavior image block, determining an abnormal behavior type which the abnormal behavior image block belongs to. Through the method provided by the invention, background modeling is carried out by use of the hybrid Gauss model, a foreground area and a background area can be accurately segmented, and abnormal behaviors in video monitoring images can be accurately detected.

Description

technical field [0001] The invention relates to the technical field of abnormal behavior detection, in particular to a video monitoring abnormal behavior detection method and a detection system thereof. Background technique [0002] The purpose of video surveillance is to detect and analyze abnormal events or the behavior of monitored objects in the surveillance scene. At present, more mature video abnormal behavior detection includes behaviors such as crossing the border, invading restricted areas, wandering, staying, and fast movement. There are usually two types of implementation methods for abnormal behavior detection: (1) behaviors with small probability or contrary to prior rules are regarded as abnormal behaviors; (2) behaviors that do not match the patterns of known normal behaviors are regarded as abnormal behaviors . [0003] In recent years, the top international journal IEEEET-PAMI and the top international conferences on computer vision and pattern recognition...

AI Technical Summary

Problems solved by technology

However, in the method proposed in this paper, when obtaining the foreground pixels, only the background difference division method is used, and the continuously changing pixels are considered to be the foreground, and the continuous static pixels are the background. This criterion is too simple. For Complex backgrounds and changing scenes are likely to cause adverse consequences such as false alarms, missed alarms, and difficulty in tracking

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