Reconstructing 3D human body pose from stereo image sequences using hierarchical human body model learning

Hee Deok Yang, Seong Whan Lee

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

12 Citations (Scopus)

Abstract

This paper presents a novel method for reconstructing a 3D human body pose using depth information based on top-down learning. The human body pose is represented by a linear combination of prototypes of 2D depth images and their corresponding 3D body models in terms of the position of a predetermined set of joints. In a 2D depth image, the optimal coefficients for a linear combination of prototypes of 2D depth images can be estimated using least square minimization. The 3D body model of the input depth image is obtained by applying the estimated coefficients to the corresponding 3D body model of prototypes. In the learning stage, the proposed method is hierarchically constructed by classifying the training data recursively into several clusters with silhouette images and depth images. In applying hierarchical human body model learning to estimate 3D human body pose, the similar pose in a silhouette image can be estimated as a different 3D human body pose. The proposed method has been tested with 20 persons' sequences. The proposed method achieved the average errors Of 12.3 degree for all human body components.

Original languageEnglish
Title of host publicationProceedings - International Conference on Pattern Recognition
Pages1004-1007
Number of pages4
Volume3
DOIs
Publication statusPublished - 2006 Dec 1
Event18th International Conference on Pattern Recognition, ICPR 2006 - Hong Kong, China
Duration: 2006 Aug 202006 Aug 24

Other

Other18th International Conference on Pattern Recognition, ICPR 2006
CountryChina
CityHong Kong
Period06/8/2006/8/24

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Hardware and Architecture
  • Electrical and Electronic Engineering

Cite this

Yang, H. D., & Lee, S. W. (2006). Reconstructing 3D human body pose from stereo image sequences using hierarchical human body model learning. In Proceedings - International Conference on Pattern Recognition (Vol. 3, pp. 1004-1007). [1699696] https://doi.org/10.1109/ICPR.2006.980