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

  • Authors:
  • Hedvig Sidenbladh;Michael J. Black;David J. Fleet

  • Affiliations:
  • -;-;-

  • Venue:
  • ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
  • Year:
  • 2000

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Abstract

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-frame assumption of brightness constancy and hence is able to track people under changing viewpoints, in grayscale image sequences, and with complex unknown backgrounds.