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基于运动矢量交点密集度的人群恐慌行为检测

钟帅¹, 蔡坚勇^1,2,3, 廖晓东^1,2,3, 黄澎¹, 张炜隽¹

(1.福建师范大学光电与信息工程学院, 福州 350007;2.福建师范大学医学光电科学与技术教育部重点实验室, 福州 350007;3.福建师范大学福建省先进光电传感与智能信息应用工程技术研究中心, 福州 350007)

Panic Crowd Behavior Detection Based on Intersection Density of Motion Vector

ZHONG Shuai¹, CAI Jian-Yong^1,2,3, LIAO Xiao-Dong^1,2,3, HUANG Peng¹, ZHANG Wei-Jun¹

(1.College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350007, China;2.Key Laboratory of Optoelectronic Science and Technology for Medicine of Ministry of Education, Fujian Normal University, Fuzhou 350007, China;3.Fujian Provincial Engineering Research Center for Optoelectronic Sensors and Intelligent Information, Fuzhou 350007, China)

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Received:October 31, 2016 Revised:January 04, 2017

中文摘要: 为了更准确有效的识别人群恐慌行为，本文提出了一种利用视频中人群运动矢量的交点密集度来判断人群恐慌异常的新算法.该算法以LK光流法为基础来提取运动人群的运动矢量信息，接着通过获得的信息求取运动矢量间的两两交叉点，然后运用分块法求得区域交叉点密集度，并以此来识别人群异常.对多个视频进行测试，测试结果表明:该算法能够以较高正确率识别视频中人群的恐慌行为.

中文关键词: 人群恐慌行人检测点密集度智能视频监控

Abstract:In order to identify the panic crowd behavior with a more accurate and effective method, a new scheme is proposed which can utilize the intersection density of motion vector in the video to judge the abnormal panic crowd behavior. This algorithm is based on LK optical flow to extract information of motion vector from moving people, and to obtain the intersection between two motion vectors, then uses divided image blocks to get the intersection density which is the key to identify abnormal crowd. Experiments on several datasets show that this algorithm can identify the panic crowd behavior with high accuracy.

keywords: panic crowd pedestrian detection intersection density intelligent video surveillance

文章编号： 中图分类号： 文献标志码：

基金项目:省科技厅区域科技重大项目（2015H4007）

Author Name	Affiliation
ZHONG Shuai	College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350007, China
CAI Jian-Yong	College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350007, China Key Laboratory of Optoelectronic Science and Technology for Medicine of Ministry of Education, Fujian Normal University, Fuzhou 350007, China Fujian Provincial Engineering Research Center for Optoelectronic Sensors and Intelligent Information, Fuzhou 350007, China
LIAO Xiao-Dong	College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350007, China Key Laboratory of Optoelectronic Science and Technology for Medicine of Ministry of Education, Fujian Normal University, Fuzhou 350007, China Fujian Provincial Engineering Research Center for Optoelectronic Sensors and Intelligent Information, Fuzhou 350007, China
HUANG Peng	College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350007, China
ZHANG Wei-Jun	College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350007, China

Author Name	Affiliation
ZHONG Shuai	College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350007, China
CAI Jian-Yong	College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350007, China Key Laboratory of Optoelectronic Science and Technology for Medicine of Ministry of Education, Fujian Normal University, Fuzhou 350007, China Fujian Provincial Engineering Research Center for Optoelectronic Sensors and Intelligent Information, Fuzhou 350007, China
LIAO Xiao-Dong	College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350007, China Key Laboratory of Optoelectronic Science and Technology for Medicine of Ministry of Education, Fujian Normal University, Fuzhou 350007, China Fujian Provincial Engineering Research Center for Optoelectronic Sensors and Intelligent Information, Fuzhou 350007, China
HUANG Peng	College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350007, China
ZHANG Wei-Jun	College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350007, China

引用文本：
钟帅,蔡坚勇,廖晓东,黄澎,张炜隽.基于运动矢量交点密集度的人群恐慌行为检测.计算机系统应用,2017,26(7):210-214
ZHONG Shuai,CAI Jian-Yong,LIAO Xiao-Dong,HUANG Peng,ZHANG Wei-Jun.Panic Crowd Behavior Detection Based on Intersection Density of Motion Vector.COMPUTER SYSTEMS APPLICATIONS,2017,26(7):210-214