Developing context sensitive HMM gesture recognition

被引:0
|
作者
Sage, K [1 ]
Howell, AJ [1 ]
Buxton, H [1 ]
机构
[1] Univ Sussex, Sch Cognit & Comp Sci, Brighton BN1 9QH, E Sussex, England
关键词
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
We are interested in methods for building cognitive vision systems to understand activities of expert operators for our ActIPret System. Our approach to the gesture recognition required here is to learn the generic models and develop methods for contextual bias of the visual interpretation in the online system. The paper first introduces issues in the development of such flexible and robust gesture learning and recognition, with a brief discussion of related research. Second, the computational model for the Hidden Markov Model (HMM) is described and results with varying amounts of noise in the training and testing phases are given. Third, extensions of this work to allow both top-down bias in the contextual processing and bottom-up augmentation by moment to moment observation of the hand trajectory are described.
引用
收藏
页码:277 / 287
页数:11
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