Tracking Human Pose Using Max-Margin Markov Models

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

	Tracking Human Pose Using Max-Margin Markov Models
	Zhao, Lin 1; Gao, Xinbo 2; Tao, Dacheng3,4 ; Li, Xuelong5
刊名	ieee transactions on image processing
	2015-12-01
卷号	24 期号:12 页码:5274-5287
关键词	Pose tracking pose estimation max-margin articulated shapes
英文摘要	we present a new method for tracking human pose by employing max-margin markov models. representing a human body by part-based models, such as pictorial structure, the problem of pose tracking can be modeled by a discrete markov random field. considering max-margin markov networks provide an efficient way to deal with both structured data and strong generalization guarantees, it is thus natural to learn the model parameters using the max-margin technique. since tracking human pose needs to couple limbs in adjacent frames, the model will introduce loops and will be intractable for learning and inference. previous work has resorted to pose estimation methods, which discard temporal information by parsing frames individually. alternatively, approximate inference strategies have been used, which can overfit to statistics of a particular data set. thus, the performance and generalization of these methods are limited. in this paper, we approximate the full model by introducing an ensemble of two tree-structured sub-models, markov networks for spatial parsing and markov chains for temporal parsing. both models can be trained jointly using the max-margin technique, and an iterative parsing process is proposed to achieve the ensemble inference. we apply our model on three challengeable data sets, which contains highly varied and articulated poses. comprehensive experimental results demonstrate the superior performance of our method over the state-of-the-art approaches.
WOS标题词	science & technology ; technology
类目[WOS]	computer science, artificial intelligence ; engineering, electrical & electronic
研究领域[WOS]	computer science ; engineering
关键词[WOS]	action recognition ; pictorial structures ; flexible mixtures ; people ; video ; flow ; propagation ; parts
收录类别	SCI ; EI
语种	英语
WOS记录号	WOS:000362488900013
公开日期	2015-11-10
内容类型	期刊论文
源URL	[http://ir.opt.ac.cn/handle/181661/25434]
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, Xian 710071, Peoples R China 2.Xidian Univ, Sch Elect Engn, State Key Lab Integrated Serv Networks, Xian 710071, Peoples R China 3.Univ Technol, Ctr Quantum Computat & Intelligent Syst, Sydney, NSW 2007, Australia 4.Univ Technol, Fac Engn & Informat Technol, Sydney, NSW 2007, Australia 5.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. Tracking Human Pose Using Max-Margin Markov Models[J]. ieee transactions on image processing,2015,24(12):5274-5287.
APA	Zhao, Lin,Gao, Xinbo,Tao, Dacheng,&Li, Xuelong.(2015).Tracking Human Pose Using Max-Margin Markov Models.ieee transactions on image processing,24(12),5274-5287.
MLA	Zhao, Lin,et al."Tracking Human Pose Using Max-Margin Markov Models".ieee transactions on image processing 24.12(2015):5274-5287.