Recover human pose from monocular image under weak perspective projection

Minglei Tong, Yuncai Liu, Thomas S Huang

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

Abstract

In this paper we construct a novel human body model using convolution surface with articulated kinematic skeleton. The human body's pose and shape in a monocular image can be estimated from convolution curve through nonlinear optimization. The contribution of the paper is in three folds: Firstly, human model based convolution surface with articulated skeletons is presented and its shape is deformable when changing polynomial parameters and radius parameters. Secondly, we give convolution surface and curve correspondence theorem under weak perspective projection, which provide a bridge between the 3D pose and 2D contour. Thirdly, we model the human body's silhouette with convolution curve in order to estimate joint's parameters from monocular images. Evalution of the method is performed on a sequence of video frames about a walking man.

Original languageEnglish (US)
Title of host publicationComputer Vision in Human-Computer Interaction - ICCV 2005 Workshop on HCI, Proceedings
Pages36-46
Number of pages11
DOIs
StatePublished - Dec 1 2005
EventICCV 2005 Workshop on HCI - Computer Vision in Human-Computer Interaction - Beijing, China
Duration: Oct 21 2005Oct 21 2005

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3766 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

OtherICCV 2005 Workshop on HCI - Computer Vision in Human-Computer Interaction
CountryChina
CityBeijing
Period10/21/0510/21/05

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Tong, M., Liu, Y., & Huang, T. S. (2005). Recover human pose from monocular image under weak perspective projection. In Computer Vision in Human-Computer Interaction - ICCV 2005 Workshop on HCI, Proceedings (pp. 36-46). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3766 LNCS). https://doi.org/10.1007/11573425_4