Learning a Tracking and Estimation Integrated Graphical Model for Human Pose Tracking

CORC > 西安光学精密机械研究所 > 中国科学院西安光学精密机械研究所 > 光学影像学习与分析中心

	Learning a Tracking and Estimation Integrated Graphical Model for Human Pose Tracking
	Zhao, Lin 1; Gao, Xinbo 1; Tao, Dacheng2,3 ; Li, Xuelong4
刊名	ieee transactions on neural networks and learning systems
	2015-12-01
卷号	26 期号:12 页码:3176-3186
关键词	Pictorial structure (PS) pose estimation pose tracking visual tracking
ISSN号	2162237x
产权排序	4
英文摘要	we investigate the tracking of 2-d human poses in a video stream to determine the spatial configuration of body parts in each frame, but this is not a trivial task because people may wear different kinds of clothing and may move very quickly and unpredictably. the technology of pose estimation is typically applied, but it ignores the temporal context and cannot provide smooth, reliable tracking results. therefore, we develop a tracking and estimation integrated model (teim) to fully exploit temporal information by integrating pose estimation with visual tracking. however, joint parsing of multiple articulated parts over time is difficult, because a full model with edges capturing all pairwise relationships within and between frames is loopy and intractable. in previous models, approximate inference was usually resorted to, but it cannot promise good results and the computational cost is large. we overcome these problems by exploring the idea of divide and conquer, which decomposes the full model into two much simpler tractable submodels. in addition, a novel two-step iteration strategy is proposed to efficiently conquer the joint parsing problem. algorithmically, we design teim very carefully so that: 1) it enables pose estimation and visual tracking to compensate for each other to achieve desirable tracking results; 2) it is able to deal with the problem of tracking loss; and 3) it only needs past information and is capable of tracking online. experiments are conducted on two public data sets in the wild with ground truth layout annotations, and the experimental results indicate the effectiveness of the proposed teim framework.
WOS标题词	science & technology ; technology
类目[WOS]	computer science, artificial intelligence ; computer science, hardware & architecture ; computer science, theory & methods ; engineering, electrical & electronic
研究领域[WOS]	computer science ; engineering
关键词[WOS]	discriminant-analysis ; visual tracking ; pictorial structures ; flexible mixtures ; recognition ; appearance ; retrieval ; parts
收录类别	SCI ; EI
语种	英语
WOS记录号	WOS:000365312800017
内容类型	期刊论文
源URL	[http://ir.opt.ac.cn/handle/181661/27549]
专题	西安光学精密机械研究所_光学影像学习与分析中心
作者单位	1.Xidian Univ, Sch Elect Engn, Video & Image Proc Syst VIPS Lab, Xian 710071, Peoples R China 2.Univ Technol Sydney, Ctr Quantum Computat & Intelligent Syst, Ultimo, NSW 2007, Australia 3.Univ Technol Sydney, Fac Engn & Informat Technol, Ultimo, NSW 2007, Australia 4.Chinese Acad Sci, Xian Inst Opt & Precis Mech, State Key Lab Transient Opt & Photon, Ctr OpT IMagery Anal & Learning OPTIMAL, Xian 710119, Shaanxi, Peoples R China
推荐引用方式 GB/T 7714	Zhao, Lin,Gao, Xinbo,Tao, Dacheng,et al. Learning a Tracking and Estimation Integrated Graphical Model for Human Pose Tracking[J]. ieee transactions on neural networks and learning systems,2015,26(12):3176-3186.
APA	Zhao, Lin,Gao, Xinbo,Tao, Dacheng,&Li, Xuelong.(2015).Learning a Tracking and Estimation Integrated Graphical Model for Human Pose Tracking.ieee transactions on neural networks and learning systems,26(12),3176-3186.
MLA	Zhao, Lin,et al."Learning a Tracking and Estimation Integrated Graphical Model for Human Pose Tracking".ieee transactions on neural networks and learning systems 26.12(2015):3176-3186.