Learning Human Actions by Combining Global Dynamics and Local Appearance

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	Learning Human Actions by Combining Global Dynamics and Local Appearance
	Luo, Guan1 ; Yang, Shuang1 ; Tian, Guodong 1; Yuan, Chunfeng1 ; Hu, Weiming1 ; Maybank, Stephen J.2; weiming hu
发表期刊	IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
	2014-12-01
卷号	36 期号:12 页码:2466-2482
文章类型	Article
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.
关键词	Action Recognition Linear Dynamical System Local Spatio-temporal Feature Non-vector Descriptor Distance Learning
WOS标题词	Science & Technology ; Technology
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研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号	WOS:000344988000011
引用统计	被引频次：21[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/3273
专题	多模态人工智能系统全国重点实验室_视频内容安全
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
第一作者单位	模式识别国家重点实验室
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.

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