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.

• Type: Conference
• Year: 2003
• Where: CVPR
• Authors: Ahmed M. Elgammal, Vinay D. Shet, Yaser Yacoob, Larry S. Davis
• Keywords: Computer Vision, CVPR 2003, Discrete Hmm, HMM Observation Model, HMM States, Non-parametric Hmm Approach, Nonparametric Hmm