Crowd behavior model analysis and abnormal behavior detection method under geographical environment

Abstract

The invention discloses a crowd behavior model analysis and abnormal behavior detection method under a geographical environment. The method comprises the following steps that: video monitoring signals can be captured, and crowd movement regions in a monitoring scene are set, and video monitoring crowd images are obtained, and geographic spatial mapping processing is performed on the crowd movement regions; measurable crowd movement fields are calculated through using an optical flow method under geographic reference, and the crowd movement fields are converted and mapped to polar coordinate reference; according to the distribution situation of the crowd movement fields under the polar coordinate reference, statistical analysis is performed on the crowd movement fields at each main direction of the polar coordinate reference, and then, crowd movement models and crowd movement trends under the geographical environment can be judged, and crowd movement rate at each main direction can be estimated; and based on the analysis results of the crowd movement models, movement trends and movement rate, detection on crowd abnormal behaviors such as movement rate mutation, movement trend mutation, reverse walking, sudden aggregation and sudden scatter is performed. The crowd behavior model analysis and abnormal behavior detection method of the invention can be widely used in areas where crowds are prone to aggregation.

Description

technical field [0001] The invention relates to a group behavior pattern analysis and abnormal behavior detection method in a geographical environment, specifically, a method that uses video data to analyze the movement state of a group of people in a geographical environment, and can detect abnormal behavior of related groups in a geographical environment Methods. Background technique [0002] With the rapid development of social economy, large-scale crowd gathering activities such as entertainment activities, exhibition activities, and sports events appear frequently, and excessive crowd gathering often leads to emergencies such as crowding and stampede. Video itself has the characteristics of time and space, intuitive expression, rich information, and dynamic real-time. In recent years, surveillance probes have spread all over the city, and the use of video surveillance to understand group behavior has become a research hotspot. Therefore, using video data to monitor the...

AI Technical Summary

Problems solved by technology

The extraction of crowd motion features is only for the image space, and the group behavior pattern is analyzed under the image coordinate reference. The group behavior ca

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