Video analysis based abnormal behavior detection method and system

Abstract

The invention provides a video analysis based abnormal behavior detection method and system. The video analysis based abnormal behavior detection method comprises the following steps of extracting pedestrian foreground images from video frames; performing mesh generation on the video frames to divide the video frames into a plurality of mesh areas and setting the mesh areas in which the pedestrian foreground images are arranged to be movement areas; marking the movement areas through a nearest neighbor method and correlating the movement areas of the adjacent video frames; calculating light stream characteristics of the marked movement areas; obtaining a weighting direction histogram according to the light stream characteristics; calculating entropy of the weighting direction histogram; selecting a detection threshold value through a Gaussian mixture model, detecting whether an abnormal behavior is generated or not according to the detection threshold value and the entropy of the weighting direction histogram and updating the detection threshold value. The nested state machine based deduction process control method can automatically detect the abnormal behavior in a video scene, avoid the abnormal behavior disturbing the public plate order and endangering the public security and personal safety, reduce personnel workload and avoid potential risks caused by leak detection and error detection.

Description

technical field [0001] The invention relates to the field of behavior recognition, in particular to a video analysis-based abnormal behavior detection method and system. Background technique [0002] At present, most video surveillance systems are still in the traditional mode, that is, "only record but not judge", so they can only investigate abnormal situations and obtain evidence through post-event video playback. Intermittent monitoring of activities in the scene, day and night on duty, heavy workload, susceptible to human sensory fatigue, resulting in missed and false detections, loses the significance of the monitoring system for on-site real-time monitoring. On the other hand, with the expansion of the scale of the monitoring system and the massive increase in the number of videos, it is becoming more and more difficult to obtain useful information or intelligence, and the search efficiency is low, which is difficult to meet the needs of the monitoring system. Conte...

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Problems solved by technology

[0003] Aiming at the problem that the traditional video surveillance system cannot judge abnormal behavior and alarm in real time, the present inventio

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