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IJMLC 2013 Vol.3(5): 430-434 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2013.V3.354

Real-Time 3D Motion Recognition of Skeleton Animation Data Stream

Jianchao Lv and Shuangjiu Xiao

Abstract—In this paper, a method for real-time 3D motion recognition based on a hierarchical recognition framework is presented. To facilitate the recognition process, motions are divided into three levels by duration and complexity. SVD (Singular Value Decomposition) is used to extract the feature vector of each motion matrix, and SVM(Support Vector Machine) is utilized to do the training and classification of the first level of motion(sub-motion). In motion recognition process, the sequence of recognized candidate sub-motions is analyzed by HMM (Hidden Markov Model) to gain certain robustness, then we recognize the second level of motions by pattern matching in this sequence. Finally a grammar-based motion synthesization is applied using motions as semantic terms to recognize the third level of motions. Experimental results show that the proposed method has high performance in sensitivity, accuracy, specialty and efficiency.

Index Terms—Motion recognition, support vector machine, singular value decomposition, grammar, classification.

The authors are with Shanghai Jiaotong University/School of Software, Shanghai, P. R. China (e-mail: lvjianchao@sjtu.edu.cn, xsjiu99@cs.sjtu.edu.cn).


Cite:Jianchao Lv and Shuangjiu Xiao, "Real-Time 3D Motion Recognition of Skeleton Animation Data Stream," International Journal of Machine Learning and Computing vol.3, no. 5, pp. 430-434, 2013.

General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
  • E-mail: ijmlc@ejournal.net

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