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Learning generative models for multi-activity body pose estimation.

T. Jaeggli, E. Koller-Meier, and L. Van Gool
IJCV
2008, in press

Abstract

We consider the problem of monocular 3d body pose tracking from video sequences. This task is inherently ambiguous. We propose to learn a generative model of the relationship of body pose and image appearance using a sparse kernel regressor. Within a particle filtering framework, the potentially multimodal posterior probability distributions can then be inferred. The 2d bounding box location of the person in the image is estimated along with its body pose. Body poses are modelled on a low-dimensional manifold, obtained by LLE dimensionality reduction. In addition to the appearance model, we learn a prior model of likely body poses and a nonlinear dynamical model, making both pose and bounding box estimation more robust. The approach is evaluated on a number of challenging video sequences, showing the ability of the approach to deal with low-resolution images and noise.


Link to publisher's page
@Article{eth_biwi_00610,
  author = {T. Jaeggli and E. Koller-Meier and and L. Van Gool},
  title = {Learning generative models for multi-activity body pose estimation. },
  journal = {IJCV},
  year = {2008},
  month = {},
  pages = {},
  volume = {},
  number = {},
  keywords = {},
  note = {in press}
}