An improved body action recognition method based on manifold learning - Details

Author：

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

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Scopus

Abstract：

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.

Keyword：

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

Author Community：

[ 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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Source ：

2015 IEEE International Conference on Mechatronics and Automation, ICMA 2015

Year： 2015

Page： 1697-1702

Language： English

Cited Count：

WoS CC Cited Count： 0

SCOPUS Cited Count： 2

ESI Highly Cited Papers on the List： 0 Unfold All

WanFang Cited Count：

Chinese Cited Count：

30 Days PV： 6

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