Tracking Human Pose Using Max-Margin Markov Models
文献类型：期刊论文
| 作者 | Zhao, Lin1; Gao, Xinbo2; Tao, Dacheng3,4 ; Li, Xuelong5
|
| 刊名 | ieee transactions on image processing
 |
| 出版日期 | 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 |
| 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. |
入库方式： OAI收割
来源：西安光学精密机械研究所
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