A Hidden Markov Model-based continuous gesture recognition system for hand motion trajectory
A Hidden Markov Model-based continuous gesture recognition system for hand motion trajectory
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DOI：
10.1109/icpr.2008.4761080
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发表时间：
2008-12
期刊：
影响因子：
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通讯作者：
M. Elmezain;A. Al-Hamadi;Jörg Appenrodt;B. Michaelis
中科院分区：
文献类型：
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作者：
M. Elmezain;A. Al-Hamadi;Jörg Appenrodt;B. Michaelis
在本文中，我们提出了一个自动系统，识别孤立和连续的手势阿拉伯数字（0-9）的实时隐马尔可夫模型（HMM）的基础上。为了处理孤立的手势，HMM使用遍历，左-右（LR）和左-右带状（LRB）的拓扑结构与不同数量的状态范围从3到10的应用。从时空轨迹中提取方向动态特征，并对其进行量化生成码字。通过我们的静态速度运动零码字检测的新颖思想来识别连续手势。因此，LRB拓扑结构结合前向算法表现出最好的性能，并达到平均识别率98.94%和95.7%，分别为孤立和连续的手势。
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.