This article is part of the series Video Analysis for Human Behavior Understanding.

Open Access Research Article

A Two-Stage Bayesian Network Method for 3D Human Pose Estimation from Monocular Image Sequences

Yuan-Kai Wang* and Kuang-You Cheng

Author Affiliations

Department of Electrical Engineering, Fu Jen Catholic University, 24205, Taipei County, Taiwan

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EURASIP Journal on Advances in Signal Processing 2010, 2010:761460  doi:10.1155/2010/761460


The electronic version of this article is the complete one and can be found online at: http://asp.eurasipjournals.com/content/2010/1/761460


Received:30 November 2009
Accepted:5 March 2010
Published:18 April 2010

© 2010 The Author(s)

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This paper proposes a novel human motion capture method that locates human body joint position and reconstructs the human pose in 3D space from monocular images. We propose a two-stage framework including 2D and 3D probabilistic graphical models which can solve the occlusion problem for the estimation of human joint positions. The 2D and 3D models adopt directed acyclic structure to avoid error propagation of inference. Image observations corresponding to shape and appearance features of humans are considered as evidence for the inference of 2D joint positions in the 2D model. Both the 2D and 3D models utilize the Expectation Maximization algorithm to learn prior distributions of the models. An annealed Gibbs sampling method is proposed for the two-stage method to inference the maximum posteriori distributions of joint positions. The annealing process can efficiently explore the mode of distributions and find solutions in high-dimensional space. Experiments are conducted on the HumanEva dataset with image sequences of walking motion, which has challenges of occlusion and loss of image observations. Experimental results show that the proposed two-stage approach can efficiently estimate more accurate human poses.

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