Gaussian Processes for Monocular 3D People tracking

author: Raquel Urtasun, Department of Computer Science, University of Toronto
published: Feb. 25, 2007,   recorded: June 2006,   views: 3328
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Description

We advocate the use of Gaussian Processes (GPs) to learn prior models of human pose and motion for 3D people tracking. The Gaussian Process Latent variable model (GPLVM) provides a low-dimensional embedding of the human pose, and defines a density function that gives higher probability to poses close to the training data. The Gaussian Process Dynamical Model (GPDM) provides also a complex dynamical model in terms of another GP. With the use of Bayesian model averaging both GPLVM and GPDM can be learned from relatively small amounts of training data, and they generalize gracefully to motions outside the training set. We show that such priors are effective for tracking a range of human walking styles, despite weak and noisy image measurements and a very simple image likelihood. Tracking is formulated in terms of a MAP estimator on short sequences of poses within a sliding temporal window.

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Reviews and comments:

Comment1 Rohan, January 13, 2009 at 10:48 a.m.:

the wmv link is broken at 3 mins or so ,could you guys fix it please?

Thanks
Rohan

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