一种基于运动相似熵的人群异常行为检测

李斐; 陈恳; 李萌; 郭春梅

doi:10.11959/j.issn.1000−0801.2017117

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一种基于运动相似熵的人群异常行为检测

研究与开发 | 更新时间：2024-06-05

- 一种基于运动相似熵的人群异常行为检测
- Crowd abnormal behavior detection based on motion similar entropy
- 电信科学 2017年33卷第5期页码：90-98
- 作者机构：
- 作者简介：
  
  [ "李斐（1992−），女，宁波大学信息科学与工程学院硕士生，主要研究方向为视频图像处理、监控视频中的人群异常分析与检测、多媒体通信技术。" ]
  [ "陈恳（1962−），男，宁波大学信息科学与工程学院副教授、硕士生导师，在核心期刊和重要国际会议发表论文100 多篇，参与和主持国家级、省部级、市厅级和校级科研项目共16 项，获得相关科研相关奖项3 项，主要研究方向为图像及视频信息处理、多媒体通信、智能控制。" ]
  [ "李萌（1992−），女，宁波大学信息科学与工程学院硕士生，主要研究方向为监控视频中的人群异常分析与检测、多媒体通信技术。" ]
  [ "郭春梅（1990−），女，宁波大学信息科学与工程学院硕士生，主要研究方向为视频跟踪、多媒体通信技术。" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(60972063);宁波市自然科学基金资助项目;The Natural Science Foundation of Ningbo of China(2014A610065);宁波大学科研基金（理）/学科资助项目;Scientific Research Foundation of Ningbo University(XKXL1308)
- DOI：10.11959/j.issn.1000−0801.2017117
  中图分类号： TP391
- 网络出版日期：2017-05，
  
  纸质出版日期：2017-05-15
- 稿件说明：
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李斐, 陈恳, 李萌, 等. 一种基于运动相似熵的人群异常行为检测[J]. 电信科学, 2017,33(5):90-98.

Fei LI, Ken CHEN, Meng LI, et al. Crowd abnormal behavior detection based on motion similar entropy[J]. Telecommunications science, 2017, 33(5): 90-98.
李斐, 陈恳, 李萌, 等. 一种基于运动相似熵的人群异常行为检测[J]. 电信科学, 2017,33(5):90-98. DOI： 10.11959/j.issn.1000−0801.2017117.

Fei LI, Ken CHEN, Meng LI, et al. Crowd abnormal behavior detection based on motion similar entropy[J]. Telecommunications science, 2017, 33(5): 90-98. DOI： 10.11959/j.issn.1000−0801.2017117.

摘要

实现对人群异常事件的检测是图形处理在智能视频监控领域的重要研究内容。提出了一种基于运动相似性熵（EMS）的人群异常行为检测算法。该算法在对视频图像进行光流计算的基础上，以底层光流块为基本单位获取场景运动信息，根据社会网络模型的概念，提出构建场景的运动网络模型（MNM），完成对场景粒子运动相似性的划分，并在时间域上计算MNM的粒子分布熵值EMS，最后将得到的图像熵与设置合理的阈值相比，判断异常行为是否发生。实验证明，该算法可有效检测异常行为，与其他经典检测算法相比有较大优势。

Abstract

It is an important research content of graphic processing in the field of intelligent video surveillance to detect abnormal events.An algorithm based on entropy of motion similarity (EMS) to detect abnormal behavior was proposed.Based on the optical flow algorithm

taking the bottom flow block as the basic unit to get the scene motion information

according to the concept of social network model

the construction scene of the motion network model (MNM) was proposed

the division of the scene particles motion similarity was completed

and the distribution EMS of MNM was calculated in the time domain.Finally

the obtained image entropy was compared with the reasonable threshold

to determine whether abnormal behavior occured.Experimental results indicate that the proposed algo-rithm can detect abnormal behavior effectively and show promising performance while comparing with the state of the art methods.

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references

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