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2010
IEEE

Dynamical Binary Latent Variable Models for 3D Human Pose Tracking

11 years 9 months ago
Dynamical Binary Latent Variable Models for 3D Human Pose Tracking
We introduce a new class of probabilistic latent variable model called the Implicit Mixture of Conditional Restricted Boltzmann Machines (imCRBM) for use in human pose tracking. Key properties of the imCRBM are as follows: (1) learning is linear in the number of training exemplars so it can be learned from large datasets; (2) it learns coherent models of multiple activities; (3) it automatically discovers atomic “movemes”; and (4) it can infer transitions between activities, even when such transitions are not present in the training set. We describe the model and how it is learned and we demonstrate its use in the context of Bayesian filtering for multi-view and monocular pose tracking. The model handles difficult scenarios including multiple activities and transitions among activities. We report state-of-the-art results on the HumanEva dataset.
Graham Taylor, Leonid Sigal, David Fleet, Geoffrey
Added 04 Apr 2010
Updated 14 May 2010
Type Conference
Year 2010
Where CVPR
Authors Graham Taylor, Leonid Sigal, David Fleet, Geoffrey Hinton
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