87 search results - page 1 / 18 » 3D People Tracking with Gaussian Process Dynamical Models |
CVPR
2006
IEEE
14 years 6 months ago
2006
IEEE
We advocate the use of Gaussian Process Dynamical Models (GPDMs) for learning human pose and motion priors for 3D people tracking. A GPDM provides a lowdimensional embedding of hu...
ICIP
2007
IEEE
14 years 6 months ago
2007
IEEE
Tracking 3D people from monocular video is often poorly constrained. To mitigate this problem, prior knowledge should be exploited. In this paper, the Gaussian process spatio-temp...
ICIP
2007
IEEE
14 years 6 months ago
2007
IEEE
This paper introduces a framework to track 3D human movement using Gaussian process dynamic model (GPDM) and particle filter. The framework combines the particle filter and discri...
GW
2009
Springer
13 years 2 months ago
2009
Springer
A challenge for 3D motion capture by monocular vision is 3D-2D projection ambiguities that may bring incorrect poses during tracking. In this paper, we propose improving 3D motion ...
ICCV
2005
IEEE
13 years 10 months ago
2005
IEEE
We advocate the use of Scaled Gaussian Process Latent Variable Models (SGPLVM) to learn prior models of 3D human pose for 3D people tracking. The SGPLVM simultaneously optimizes a...