PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Simultaneous Segmentation and Pose Estimation of Humans Using Dynamic Graph Cuts
Pushmeet Kohli, Jon Rihan, Matthieu Bray and Philip Torr
International Journal of Computer Vision (IJCV) Volume 79, Number 3, pp. 285-298, 2008.

Abstract

This paper presents a novel algorithm for performing integrated segmentation and 3D pose estimation of a human body from multiple views. Unlike other state of the art methods which focus on either segmentation or pose estimation individually, our approach tackles these two tasks together. Our method works by optimizing a cost function based on a Conditional Random Field (CRF). This has the advantage that all information in the image (edges, background and foreground appearances), as well as the prior information on the shape and pose of the subject can be combined and used in a Bayesian framework. Optimizing such a cost function would have been computationally infeasible. However, our recent research in dynamic graph cuts allows this to be done much more efficiently than before. We demonstrate the efficacy of our approach on challenging motion sequences. Although we target the human pose inference problem in the paper, our method is completely generic and can be used to segment and infer the pose of any rigid, deformable or articulated object.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
Learning/Statistics & Optimisation
ID Code:5450
Deposited By:Karteek Alahari
Deposited On:29 August 2009