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 |
DOI | 10.1109/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 | |
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 |
推荐引用方式 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. |
条目包含的文件 | ||||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Learning Discriminat(1028KB) | 期刊论文 | 出版稿 | 限制开放 | CC BY | 请求全文 |
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