Real-time simultaneous pose and shape estimation for articulated objects using a single depth camera

Mao Ye, Ruigang Yang

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

130 Scopus citations

Abstract

In this paper we present a novel real-time algorithm for simultaneous pose and shape estimation for articulated objects, such as human beings and animals. The key of our pose estimation component is to embed the articulated deformation model with exponential-maps-based parametrization into a Gaussian Mixture Model. Benefiting from the probabilistic measurement model, our algorithm requires no explicit point correspondences as opposed to most existing methods. Consequently, our approach is less sensitive to local minimum and well handles fast and complex motions. Extensive evaluations on publicly available datasets demonstrate that our method outperforms most state-of-art pose estimation algorithms with large margin, especially in the case of challenging motions. Moreover, our novel shape adaptation algorithm based on the same probabilistic model automatically captures the shape of the subjects during the dynamic pose estimation process. Experiments show that our shape estimation method achieves comparable accuracy with state of the arts, yet requires neither parametric model nor extra calibration procedure.

Original languageEnglish
Title of host publicationProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Pages2353-2360
Number of pages8
ISBN (Electronic)9781479951178, 9781479951178
DOIs
StatePublished - Sep 24 2014
Event27th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014 - Columbus, United States
Duration: Jun 23 2014Jun 28 2014

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ISSN (Print)1063-6919

Conference

Conference27th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014
Country/TerritoryUnited States
CityColumbus
Period6/23/146/28/14

Bibliographical note

Publisher Copyright:
© 2014 IEEE.

Keywords

  • Articulated Pose Estimation
  • Depth Cameras
  • Shape Estimation

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

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