期刊
PATTERN RECOGNITION
卷 43, 期 9, 页码 3059-3072出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.patcog.2010.03.016
关键词
Hand gestures recognition; Dynamic Bayesian network; Coupled hidden Markov model; Continuous gesture spotting
资金
- Korean Government (MOEHRD) [KRF-2006-311-D00197]
- Ministry of Knowledge Economy of Korea
- Korea Institute of Industrial Technology(KITECH) [6-1] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
In this paper, we propose a new method for recognizing hand gestures in a continuous video stream using a dynamic Bayesian network or DBN model. The proposed method of DBN-based inference is preceded by steps of skin extraction and modelling, and motion tracking. Then we develop a gesture model for one- or two-hand gestures. They are used to define a cyclic gesture network for modeling continuous gesture stream. We have also developed a DP-based real-time decoding algorithm for continuous gesture recognition. In our experiments with 10 isolated gestures, we obtained a recognition rate upwards of 99.59% with cross validation. In the case of recognizing continuous stream of gestures, it recorded 84% with the precision of 80.77% for the spotted gestures. The proposed DBN-based hand gesture model and the design of a gesture network model are believed to have a strong potential for successful applications to other related problems such as sign language recognition although it is a bit more complicated requiring analysis of hand shapes. (C) 2010 Elsevier Ltd. All rights reserved.
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