Motion capturing from monocular vision by statistical inference based on motion database: Vector field approach

Dongheui Lee, Yoshihiko Nakamura

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Scopus citations

Abstract

This paper proposes a 3D motion recovery method from monocular images by statistical inference. The fundamental idea of the paper originates from the mimesis model, inspired by the mirror neuron system. The mimesis model is extended to include motion understanding from monocular image sequences and to imitate whole-body motion patterns in 3D space. In order to achieve this goal, (1) conversion of 3D motion database, represented in probabilistic form, into various spaces is adopted. (2) A vector field approach is developed for natural motion understanding. (3) With the particle filter, a demonstrator's pose is estimated.

Original languageEnglish
Title of host publicationProceedings of the 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2007
Pages617-623
Number of pages7
DOIs
StatePublished - 2007
Externally publishedYes
Event2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2007 - San Diego, CA, United States
Duration: 29 Oct 20072 Nov 2007

Publication series

NameIEEE International Conference on Intelligent Robots and Systems

Conference

Conference2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2007
Country/TerritoryUnited States
CitySan Diego, CA
Period29/10/072/11/07

Keywords

  • Monocular vision
  • Motion capturing
  • Particle filter
  • Statistical inference
  • Vector field

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