Gaussian Processes for Monocular 3D People tracking
published: Feb. 25, 2007, recorded: June 2006, views: 3360
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
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.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !