%0 Conference Paper
%B Computer Vision and Pattern Recognition, 2003. Proceedings. 2003 IEEE Computer Society Conference on
%D 2003
%T Learning dynamics for exemplar-based gesture recognition
%A Elgammal,A.
%A Shet,V.
%A Yacoob,Yaser
%A Davis, Larry S.
%K arbitrary
%K body
%K by
%K Computer
%K constraint;
%K detection;
%K discrete
%K distribution
%K dynamics;
%K edge
%K estimation;
%K example;
%K exemplar
%K exemplar-based
%K extraction;
%K feature
%K framework;
%K gesture
%K gesture;
%K hidden
%K HMM;
%K human
%K image
%K learning
%K Markov
%K matching;
%K model;
%K models;
%K motion;
%K nonparametric
%K pose
%K probabilistic
%K recognition;
%K sequence;
%K space;
%K state;
%K statistics;
%K system
%K temporal
%K tool;
%K view-based
%K vision;
%X 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 non-parametric HMM.
%B Computer Vision and Pattern Recognition, 2003. Proceedings. 2003 IEEE Computer Society Conference on
%V 1
%P I-571 - I-578 vol.1 - I-571 - I-578 vol.1
%8 2003/06//
%G eng
%R 10.1109/CVPR.2003.1211405