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...

• Type: Conference
• Year: 2000
• Where: ECCV
• Authors: Hedvig Sidenbladh, Michael J. Black, David J. Fleet
• Keywords: Computer Vision, ECCV 2000, Explicit Posterior Probability, Grayscale Image Sequences, Monocular Image Sequences, Posterior Probability Distribution, Prior Probability Distribution