View invariant body pose estimation based on biased manifold learning

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

6 Citations (Scopus)

Abstract

In human body pose estimation, manifold learning is a popular technique for reducing the dimension of 2D images and 3D body configuration data. This technique, however, is especially vulnerable to silhouette variation such as caused by viewpoint changes. In this paper, we propose a novel approach that combines three separate manifolds for representing variations in viewpoint, pose and 3D body configuration. We use biased manifold learning to learn these manifolds with appropriately weighted distances. A set of four mapping functions are then learned by a generalized regression neural network for added robustness. Despite using only three manifolds, we show that this method can reliably estimate 3D body poses from 2D images with all learned viewpoints.

Original languageEnglish
Title of host publicationProceedings - 2010 20th International Conference on Pattern Recognition, ICPR 2010
Pages3866-3869
Number of pages4
DOIs
Publication statusPublished - 2010
Event2010 20th International Conference on Pattern Recognition, ICPR 2010 - Istanbul, Turkey
Duration: 2010 Aug 232010 Aug 26

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651

Other

Other2010 20th International Conference on Pattern Recognition, ICPR 2010
CountryTurkey
CityIstanbul
Period10/8/2310/8/26

Keywords

  • Body pose analysis
  • Manifold learning
  • Non-linear dimensional reduction
  • Supervised learning
  • View-invariance

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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