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A Hidden Markov Model-based continuous gesture recognition system for hand motion trajectory

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4 Author(s)
Elmezain, M. ; Inst. for Electron., Otto-von-Guericke-Univ. Magdeburg, Magdeburg, Germany ; Al-Hamadi, A. ; Appenrodt, J. ; Michaelis, B.

In this paper, we propose an automatic system that recognizes both isolated and continuous gestures for Arabic numbers (0-9) in real-time based on hidden Markov model (HMM). To handle isolated gestures, HMM using ergodic, left-right (LR) and left-right banded (LRB) topologies with different number of states ranging from 3 to 10 is applied. Orientation dynamic features are obtained from spatio-temporal trajectories and then quantized to generate its codewords. The continuous gestures are recognized by our novel idea of zero-codeword detection with static velocity motion. Therefore, the LRB topology in conjunction with forward algorithm presents the best performance and achieves average rate recognition 98.94% and 95.7% for isolated and continuous gestures, respectively.

Published in:

Pattern Recognition, 2008. ICPR 2008. 19th International Conference on

Date of Conference:

8-11 Dec. 2008