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Learning Human Actions by Combining Global Dynamics and Local Appearance

文献类型：期刊论文

作者Luo, Guan1; Yang, Shuang1; Tian, Guodong1; Yuan, Chunfeng1; Hu, Weiming1; Maybank, Stephen J.2; weiming hu
刊名IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
出版日期2014-12-01
卷号36期号:12页码:2466-2482
关键词Action recognition linear dynamical system local spatio-temporal feature non-vector descriptor distance learning
英文摘要In this paper, we address the problem of human action recognition through combining global temporal dynamics and local visual spatio-temporal appearance features. For this purpose, in the global temporal dimension, we propose to model the motion dynamics with robust linear dynamical systems (LDSs) and use the model parameters as motion descriptors. Since LDSs live in a non-euclidean space and the descriptors are in non-vector form, we propose a shift invariant subspace angles based distance to measure the similarity between LDSs. In the local visual dimension, we construct curved spatio-temporal cuboids along the trajectories of densely sampled feature points and describe them using histograms of oriented gradients (HOG). The distance between motion sequences is computed with the Chi-Squared histogram distance in the bag-of-words framework. Finally we perform classification using the maximum margin distance learning method by combining the global dynamic distances and the local visual distances. We evaluate our approach for action recognition on five short clips data sets, namely Weizmann, KTH, UCF sports, Hollywood2 and UCF50, as well as three long continuous data sets, namely VIRAT, ADL and CRIM13. We show competitive results as compared with current state-of-the-art methods.
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]HUMAN ACTION CATEGORIES ; BINET-CAUCHY KERNELS ; TIME INTEREST POINTS ; ACTION RECOGNITION ; HUMAN MOTION ; SUBSPACE IDENTIFICATION ; TEXTURE RECOGNITION ; MODELS ; CLASSIFICATION ; SYSTEMS
收录类别SCI
语种英语
WOS记录号WOS:000344988000011
源URL[http://ir.ia.ac.cn/handle/173211/3273]  
专题自动化研究所_模式识别国家重点实验室_视频内容安全团队
通讯作者weiming hu
作者单位1.Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Beijing 100190, Peoples R China
2.Univ London Birkbeck Coll, Dept Comp Sci & Informat Syst, London WC1E 7HX, England
推荐引用方式
GB/T 7714		 Luo, Guan,Yang, Shuang,Tian, Guodong,et al. Learning Human Actions by Combining Global Dynamics and Local Appearance[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,2014,36(12):2466-2482.
APA Luo, Guan.,Yang, Shuang.,Tian, Guodong.,Yuan, Chunfeng.,Hu, Weiming.,...&weiming hu.(2014).Learning Human Actions by Combining Global Dynamics and Local Appearance.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,36(12),2466-2482.
MLA Luo, Guan,et al."Learning Human Actions by Combining Global Dynamics and Local Appearance".IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 36.12(2014):2466-2482.

入库方式： OAI收割

来源：自动化研究所

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