Reconstruction of 3D human body pose based on top-down learning

Hee Deok Yang, Sung Kee Park, Seong Whan Lee

Research output: Contribution to journalConference article

3 Citations (Scopus)

Abstract

This paper presents a novel method for reconstructing 3D human body pose from monocular image sequences based on top-down learning. Human body pose is represented by a linear combination of prototypes of 2D silhouette images and their corresponding 3D body models in terms of the position of a predetermined set of joints. With a 2D silhouette image, we can estimate optimal coefficients for a linear combination of prototypes of the 2D silhouette images by solving least square minimization. The 3D body model of the input silhouette 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 into several clusters recursively. Also, in the reconstructing stage, the proposed method hierarchically reconstructs 3D human body pose with a silhouette image or a silhouette history image. We use a silhouette history image and a blurring silhouette image as the spatio-temporal features for reducing noise due to extraction of silhouette image and for extending the search area of current body pose to related body pose. The experimental results show that our method can be efficient and effective for reconstructing 3D human body pose.

Original languageEnglish
Pages (from-to)601-610
Number of pages10
JournalLecture Notes in Computer Science
Volume3644
Issue numberPART I
DOIs
Publication statusPublished - 2005
EventInternational Conference on Intelligent Computing, ICIC 2005 - Hefei, China
Duration: 2005 Aug 232005 Aug 26

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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