Hidden-Markov-Models-Based Dynamic Hand Gesture Recognition

被引：36
|
作者：
Wang, Xiaoyan [1 ]
Xia, Ming [1 ]
Cai, Huiwen [2 ]
Gao, Yong [3 ]
Cattani, Carlo [4 ]
机构：
[1] Zhejiang Univ Technol, Coll Comp Sci & Technol, Hangzhou 310023, Zhejiang, Peoples R China
[2] Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
[3] Zhejiang Jieshang Vis Sci & Technol Cooperat, Hangzhou 310013, Zhejiang, Peoples R China
[4] Univ Salerno, Dept Math, I-84084 Fisciano, Italy
来源：
MATHEMATICAL PROBLEMS IN ENGINEERING | 2012年 / 2012卷
关键词：
VISION;
D O I：
10.1155/2012/986134
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
This paper is concerned with the recognition of dynamic hand gestures. A method based on Hidden Markov Models (HMMs) is presented for dynamic gesture trajectory modeling and recognition. Adaboost algorithm is used to detect the user's hand and a contour-based hand tracker is formed combining condensation and partitioned sampling. Cubic B-spline is adopted to approximately fit the trajectory points into a curve. Invariant curve moments as global features and orientation as local features are computed to represent the trajectory of hand gesture. The proposed method can achieve automatic hand gesture online recognition and can successfully reject atypical gestures. The experimental results show that the proposed algorithm can reach better recognition results than the traditional hand recognition method.
引用
收藏
页数：11
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