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作者:

Zhang, P. (Zhang, P..) | Jia, S. (Jia, S..) | Xu, T. (Xu, T..) | Li, X. (Li, X..) | Xuan, X. (Xuan, X..)

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摘要:

The body action recognition is one of the key technologies of the computer vision. As the fact that the features of body action usually reside on low dimensional manifolds embedded in a high dimensional ambient space, a new method of body action recognition based on manifold learning is proposed in this paper. In the proposed method, a Linear Local Embedding of Difference (DLLE) algorithm is applied to get the low dimensional manifolds of the images and achieve human action recognition. The result shows that the DLLE method has more advantage in time-consuming and recognition accuracy rate than the other dimensionality reduction methods. Furthermore, the experimental results demonstrated the feasibility and effectiveness of the proposed algorithm in body action recognition. © 2015 IEEE.

关键词:

body action recognition; dimensionality reduction; DLLE algorithm; manifold learning

作者机构:

  • [ 1 ] [Zhang, P.]College of Electronic and Control Engineering, Beijing University of Technology, Beijing, 100124, China
  • [ 2 ] [Zhang, P.]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing, 100124, China
  • [ 3 ] [Zhang, P.]Engineering Research Center of Digital Community, Ministry of Education, Beijing, 100124, China
  • [ 4 ] [Jia, S.]College of Electronic and Control Engineering, Beijing University of Technology, Beijing, 100124, China
  • [ 5 ] [Jia, S.]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing, 100124, China
  • [ 6 ] [Jia, S.]Engineering Research Center of Digital Community, Ministry of Education, Beijing, 100124, China
  • [ 7 ] [Xu, T.]College of Electronic and Control Engineering, Beijing University of Technology, Beijing, 100124, China
  • [ 8 ] [Xu, T.]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing, 100124, China
  • [ 9 ] [Xu, T.]Engineering Research Center of Digital Community, Ministry of Education, Beijing, 100124, China
  • [ 10 ] [Li, X.]College of Electronic and Control Engineering, Beijing University of Technology, Beijing, 100124, China
  • [ 11 ] [Li, X.]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing, 100124, China
  • [ 12 ] [Li, X.]Engineering Research Center of Digital Community, Ministry of Education, Beijing, 100124, China
  • [ 13 ] [Xuan, X.]College of Electronic and Control Engineering, Beijing University of Technology, Beijing, 100124, China
  • [ 14 ] [Xuan, X.]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing, 100124, China
  • [ 15 ] [Xuan, X.]Engineering Research Center of Digital Community, Ministry of Education, Beijing, 100124, China

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来源 :

2015 IEEE International Conference on Mechatronics and Automation, ICMA 2015

年份: 2015

页码: 1697-1702

语种: 英文

被引次数:

WoS核心集被引频次: 0

SCOPUS被引频次: 2

ESI高被引论文在榜: 0 展开所有

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中文被引频次:

近30日浏览量: 2

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