Attractive People: Assembling Loose-Limbed Models using
Non-parametric Belief Propagation
Leonid Sigal, Michael Isard, Benjamin H Sigelman and Michael J
Black
Advances in Neural Information Processing Systems 16, 1539-1546 (2003)
Abstract
The detection and pose estimation of people in images and video is
made challenging by the variability of human appearance, the
complexity of natural scenes, and the high dimensionality of
articulated body models. To cope with these problems we represent the
3D human body as a graphical model in which the relationships between
the body parts are represented by conditional probability
distributions. We formulate the pose estimation problem as one of
probabilistic inference over a graphical model where the random
variables correspond to the individual limb parameters (position and
orientation). Because the limbs are described by 6-dimensional vectors
encoding pose in 3-space, discretization is impractical and the random
variables in our model must be continuousvalued. To approximate
belief propagation in such a graph we exploit a recently introduced
generalization of the particle filter. This framework facilitates the
automatic initialization of the body-model from low level cues and is
robust to occlusion of body parts and scene clutter.
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