Exploring Coherent Motion Patterns via Structured Trajectory Learning for Crowd Mood Modeling

doi:10.1109/TCSVT.2016.2593609

CORC > 计算技术研究所 > 中国科学院计算技术研究所 > 中国科学院计算技术研究所期刊论文 > 英文

	Exploring Coherent Motion Patterns via Structured Trajectory Learning for Crowd Mood Modeling
	Zhang, Yanhao 4; Qin, Lei 5; Ji, Rongrong 1; Zhao, Sicheng 4; Huang, Qingming 3,4; Luo, Jiebo 2
刊名	IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
	2017-03-01
卷号	27 期号:3 页码:635-648
关键词	Coherent motion pattern crowd behavior analysis emotional motion feature structured trajectory learning (STL)
ISSN号	1051-8215
DOI	10.1109/TCSVT.2016.2593609
英文摘要	Crowd behavior analysis has recently attracted extensive attention in research. However, the existing research mainly focuses on investigating motion patterns in crowds, while the emotional aspects of crowd behaviors are left unexplored. Analyzing the emotion of crowd behaviors is indeed extremely important, as it uncovers the social moods that are beneficial for video surveillance. In this paper, we propose a novel crowd representation termed crowd mood. Crowd mood is established based upon the discovery that the social emotional hypothesis of crowd behaviors can be revealed by investigating the spacing interactions and the structural levels of motion patterns in crowds. To this end, we first learn the structured trajectories of crowds by particle advection using low-rank approximation with group sparsity constraint, which implicitly characterizes the coherent motion patterns. Second, rich emotional motion features are explicitly extracted and fused by support vector regression to reflect social characteristics. In particular, we construct weighted features in a boosted manner by considering the features' significance. Finally, crowd mood is intuitively presented as affective curves to track the emotion states of the crowd dynamics, which is robust to noise, sensitive to semantic shift, and compact for pattern expressions. Extensive evaluations on crowd video data sets demonstrate that our approach effectively models crowd mood and achieves significantly better results with comparisons to several alternative and state-of-the-art approaches for various tasks, i. e., crowd mood classification, global abnormal mood detection, and crowd emotion matching.
资助项目	National Basic Research Program of China (973 Program)[2015CB351802] ; National Basic Research Program of China (973 Program)[2012CB316400] ; National Natural Science Foundation of China[61402388] ; National Natural Science Foundation of China[61332016] ; National Natural Science Foundation of China[61133003] ; National Natural Science Foundation of China[61390510] ; National Natural Science Foundation of China[61572465]
WOS研究方向	Engineering
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000397576200020
内容类型	期刊论文
源URL	[http://119.78.100.204/handle/2XEOYT63/7350]
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Qin, Lei
作者单位	1.Xiamen Univ, Dept Cognit Sci, Sch Informat Sci & Engn, Xiamen 361005, Peoples R China 2.Univ Rochester, Dept Comp Sci, Rochester, NY 14627 USA 3.Univ Chinese Acad Sci, Beijing 100190, Peoples R China 4.Harbin Inst Technol, Sch Comp Sci, Harbin 150001, Peoples R China 5.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Zhang, Yanhao,Qin, Lei,Ji, Rongrong,et al. Exploring Coherent Motion Patterns via Structured Trajectory Learning for Crowd Mood Modeling[J]. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,2017,27(3):635-648.
APA	Zhang, Yanhao,Qin, Lei,Ji, Rongrong,Zhao, Sicheng,Huang, Qingming,&Luo, Jiebo.(2017).Exploring Coherent Motion Patterns via Structured Trajectory Learning for Crowd Mood Modeling.IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,27(3),635-648.
MLA	Zhang, Yanhao,et al."Exploring Coherent Motion Patterns via Structured Trajectory Learning for Crowd Mood Modeling".IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 27.3(2017):635-648.