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2008
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A Hidden Markov Model-based continuous gesture recognition system for hand motion trajectory

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A Hidden Markov Model-based continuous gesture recognition system for hand motion trajectory
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 LeftRight 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.
Mahmoud Elmezain, Ayoub Al-Hamadi, Jörg Appen
Added 30 May 2010
Updated 30 May 2010
Type Conference
Year 2008
Where ICPR
Authors Mahmoud Elmezain, Ayoub Al-Hamadi, Jörg Appenrodt, Bernd Michaelis
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