Sciweavers

Share
CVPR
2003
IEEE

Learning Dynamics for Exemplar-based Gesture Recognition

10 years 4 months ago
Learning Dynamics for Exemplar-based Gesture Recognition
This paper addresses the problem of capturing the dynamics for exemplar-based recognition systems. Traditional HMM provides a probabilistic tool to capture system dynamics and in exemplar paradigm, HMM states are typically coupled with the exemplars. Alternatively, we propose a non-parametric HMM approach that uses a discrete HMM with arbitrary states (decoupled from exemplars) to capture the dynamics over a large exemplar space where a nonparametric estimation approach is used to model the exemplar distribution. This reduces the need for lengthy and non-optimal training of the HMM observation model. We used the proposed approach for view-based recognition of gestures. The approach is based on representing each gesture as a sequence of learned body poses (exemplars). The gestures are recognized through a probabilistic framework for matching these body poses and for imposing temporal constraints between different poses using the proposed nonparametric HMM.
Ahmed M. Elgammal, Vinay D. Shet, Yaser Yacoob, La
Added 12 Oct 2009
Updated 12 Oct 2009
Type Conference
Year 2003
Where CVPR
Authors Ahmed M. Elgammal, Vinay D. Shet, Yaser Yacoob, Larry S. Davis
Comments (0)
books