Supervised manifold learning based on biased distance for view invariant body pose estimation

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

In human body pose estimation, manifold learning is a useful method for reducing the dimension of 2D images and 3D body configuration data. Most commonly, body pose is estimated from silhouettes derived from images or image sequences. A major problem when applying manifold estimation, however, is its vulnerability to silhouette variation. In this paper, we propose a novel approach to solving viewpoint-induced silhouette variation by introducing biased label distances for learning manifolds that are able to represent variations in viewpoint, pose, and 3D body configuration. We demonstrate the effectiveness of the approach on a synthetic and a real-world dataset.

Original languageEnglish
Title of host publicationConference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
Pages2717-2720
Number of pages4
DOIs
Publication statusPublished - 2012 Dec 1
Event2012 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2012 - Seoul, Korea, Republic of
Duration: 2012 Oct 142012 Oct 17

Other

Other2012 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2012
CountryKorea, Republic of
CitySeoul
Period12/10/1412/10/17

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Keywords

  • Biased distance
  • Manifold learning
  • Pose estimation

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Human-Computer Interaction

Cite this

Hur, D., Wallraven, C., & Lee, S. W. (2012). Supervised manifold learning based on biased distance for view invariant body pose estimation. In Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics (pp. 2717-2720). [6378158] https://doi.org/10.1109/ICSMC.2012.6378158