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: firstname.lastname@example.org, email@example.com).
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.