Reconstruction of 3D human body pose for gait recognition

Hee Deok Yang, Seong Whan Lee

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

13 Citations (Scopus)

Abstract

In this paper, we propose a novel method to reconstruct 3D human body pose for gait recognition 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. The experimental results show that our method can be efficient and effective to reconstruct 3D human body pose for gait recognition.

Original languageEnglish
Title of host publicationAdvances in Biometrics - International Conference, ICB 2006, Proceedings
Pages619-625
Number of pages7
Publication statusPublished - 2006
EventInternational Conference on Biometrics, ICB 2006 - Hong Kong, China
Duration: 2006 Jan 52006 Jan 7

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3832 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

OtherInternational Conference on Biometrics, ICB 2006
CountryChina
CityHong Kong
Period06/1/506/1/7

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Yang, H. D., & Lee, S. W. (2006). Reconstruction of 3D human body pose for gait recognition. In Advances in Biometrics - International Conference, ICB 2006, Proceedings (pp. 619-625). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3832 LNCS).