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Learning Discriminative Key Poses for Action Recognition
Liu, Li1; Shao, Ling1,2; Zhen, Xiantong1; Li, Xuelong3
2013-12-01
发表期刊IEEE TRANSACTIONS ON CYBERNETICS
卷号43期号:6页码:1860-1870
摘要In this paper, we present a new approach for human action recognition based on key-pose selection and representation. Poses in video frames are described by the proposed extensive pyramidal features (EPFs), which include the Gabor, Gaussian, and wavelet pyramids. These features are able to encode the orientation, intensity, and contour information and therefore provide an informative representation of human poses. Due to the fact that not all poses in a sequence are discriminative and representative, we further utilize the AdaBoost algorithm to learn a subset of discriminative poses. Given the boosted poses for each video sequence, a new classifier named weighted local naive Bayes nearest neighbor is proposed for the final action classification, which is demonstrated to be more accurate and robust than other classifiers, e.g., support vector machine (SVM) and naive Bayes nearest neighbor. The proposed method is systematically evaluated on the KTH data set, the Weizmann data set, the multiview IXMAS data set, and the challenging HMDB51 data set. Experimental results manifest that our method outperforms the state-of-the-art techniques in terms of recognition rate.
文章类型Article
关键词Adaboost Computer Vision Extensive Pyramidal Features (Epfs) Human Action Recognition Pose Selection Weighted Local Naive Bayes Nearest Neighbor (Wlnbnn) Classifier
WOS标题词Science & Technology ; Technology
DOI10.1109/TSMCB.2012.2231959
收录类别SCI ; EI
关键词[WOS]WAVELET TRANSFORM ; CLASSIFICATION ; REGRESSION ; FEATURES ; POINTS
语种英语
WOS研究方向Computer Science
WOS类目Computer Science, Artificial Intelligence ; Computer Science, Cybernetics
WOS记录号WOS:000327647500029
引用统计
被引频次:69[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.opt.ac.cn/handle/181661/23438
专题光谱成像技术研究室
作者单位1.Univ Sheffield, Dept Elect & Elect Engn, Sheffield S1 3JD, S Yorkshire, England
2.Nanjing Univ Informat Sci & Technol, Coll Elect & Informat Engn, Nanjing 210044, Jiangsu, Peoples R China
3.Chinese Acad Sci, Xian Inst Opt & Precis Mech, State Key Lab Transient Opt & Photon, Ctr OPT IMagery Anal & Learning OPTIMAL, Xian 710119, Peoples R China
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GB/T 7714
Liu, Li,Shao, Ling,Zhen, Xiantong,et al. Learning Discriminative Key Poses for Action Recognition[J]. IEEE TRANSACTIONS ON CYBERNETICS,2013,43(6):1860-1870.
APA Liu, Li,Shao, Ling,Zhen, Xiantong,&Li, Xuelong.(2013).Learning Discriminative Key Poses for Action Recognition.IEEE TRANSACTIONS ON CYBERNETICS,43(6),1860-1870.
MLA Liu, Li,et al."Learning Discriminative Key Poses for Action Recognition".IEEE TRANSACTIONS ON CYBERNETICS 43.6(2013):1860-1870.
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