Perceiving Systems, Computer Vision

Attractive people: Assembling loose-limbed models using non-parametric belief propagation

2003

Conference Paper

ps


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 continuous-valued. 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.

Author(s): Sigal, L. and Isard, M. I. and Sigelman, B. H. and Black, M. J.
Book Title: Advances in Neural Information Processing Systems 16, NIPS
Pages: 1539-1546
Year: 2003
Editors: S. Thrun and L. K. Saul and B. Schölkopf
Publisher: MIT Press

Department(s): Perceiving Systems
Bibtex Type: Conference Paper (inproceedings)
Paper Type: Conference

Links: pdf (color)
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BibTex

@inproceedings{Black:NIPS:2003,
  title = {Attractive people: Assembling loose-limbed models using non-parametric belief propagation},
  author = {Sigal, L. and Isard, M. I. and Sigelman, B. H. and Black, M. J.},
  booktitle = {Advances in Neural Information Processing Systems 16, NIPS},
  pages = {1539-1546},
  editors = {S. Thrun and L. K. Saul and B. Schölkopf},
  publisher = {MIT Press},
  year = {2003},
  doi = {}
}