Sciweavers

Share
ECCV
2000
Springer

Stochastic Tracking of 3D Human Figures Using 2D Image Motion

11 years 14 days ago
Stochastic Tracking of 3D Human Figures Using 2D Image Motion
A probabilistic method for tracking 3D articulated human figures in monocular image sequences is presented. Within a Bayesian framework, we define a generative model of image appearance, a robust likelihood function based on image graylevel differences, and a prior probability distribution over pose and joint angles that models how humans move. The posterior probability distribution over model parameters is represented using a discrete set of samples and is propagated over time using particle filtering. The approach extends previous work on parameterized optical flow estimation to exploit a complex 3D articulated motion model. It also extends previous work on human motion tracking by including a perspective camera model, by modeling limb self occlusion, and by recovering 3D motion from a monocular sequence. The explicit posterior probability distribution represents ambiguities due to image matching, model singularities, and perspective projection. The method relies only on a frame-to-f...
Hedvig Sidenbladh, Michael J. Black, David J. Flee
Added 16 Oct 2009
Updated 16 Oct 2009
Type Conference
Year 2000
Where ECCV
Authors Hedvig Sidenbladh, Michael J. Black, David J. Fleet
Comments (0)
books