Abstract
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.
Original language | English |
---|---|
Pages (from-to) | 3059-3072 |
Number of pages | 14 |
Journal | Pattern Recognition |
Volume | 43 |
Issue number | 9 |
DOIs | |
Publication status | Published - 2010 Sep |
Keywords
- Continuous gesture spotting
- Coupled hidden Markov model
- Dynamic Bayesian network
- Hand gestures recognition
ASJC Scopus subject areas
- Software
- Signal Processing
- Computer Vision and Pattern Recognition
- Artificial Intelligence