PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Learning Generative Models for Monocular Body Pose Estimation
Tobias Jaeggli, Esther Koller-Meier, Luc Van Gool and Luc Van Gool
In: 8th Asian Conference on Computer Vision, 18-22 November 2007, Tokyo, Japan.

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.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
ID Code:3644
Deposited By:Luc Van Gool
Deposited On:14 February 2008