突发公共安全事件监控视频异常行为监测仿真
Simulation of Abnormal Behavior Monitoring in Surveillance Video of Sudden Public Security Incidents
杨传杰 1殷洁 2汪雁 1武文亚1
作者信息
- 1. 中国消防救援学院,北京 102202
- 2. 南通大学地理科学学院,江苏 南通 226607
- 折叠
摘要
公共安全事件监控视频中的异常目标通常为中小型个体,其监测过程也易受到周围公共场所环境的干扰.提出突发公共安全事件监控视频异常行为监测方法.建立符合人体异常行为特点的增强型Cucker-Smale群体运动模型,描述人体异常行为的目标运动函数.以上述函数为基础,采集监控视频中与突发公共安全事件相关的异常行为数据.利用光流块为单位提取异常行为数据特征,并将异常行为数据特征与K-means聚类算法结合,全面分类异常行为数据特征,实现突发公共安全事件监控视频异常行为的监测.仿真结果表明,研究方法能够准确的识别出监控视频中的异常行为,且AUC值最高可达 0.983,说明所提方法具有更好的应用性能.
Abstract
The abnormal targets in public safety event monitoring videos are usually small and medium-sized in-dividuals,and their monitoring process is also susceptible to interference from the surrounding public environment.In this paper,a method of monitoring abnormal behaviors in surveillance videos of sudden public security incidents was presented.First of all,we built an enhanced Cucker-Smale group motion model in line with the characteristics of hu-man abnormal behavior to describe the target motion function of human abnormal behavior.Based on above functions,we collected the abnormal behavior data related to sudden public security incidents in the surveillance videos.Moreo-ver,we used the optical flow block as a unit,and extracted the abnormal behavior data features.And then,these fea-tures were combined with K-means clustering algorithm.Finally,we comprehensively classified the abnormal behavior data features,thus achieving the monitoring on abnormal behavior in surveillance videos of sudden public security in-cidents.Simulation results show that the proposed method can identify the abnormal behavior in surveillance video ac-curately.The AUC value is up to 0.983.Therefore,the method has better application performance.
关键词
突发公共安全事件/监控视频/异常行为/光流块/聚类算法Key words
Sudden public security incidents/Surveillance video/Abnormal behavior/Optical flow block/Clustering algorithm引用本文复制引用
基金项目
南通市社科基金项目(2021BNT022)
出版年
2024
NETL
NSTL
维普
万方数据