Human motion capture data recovery via trajectory-based sparse representation

Junhui Hou, Lap Pui Chau, Ying He, Jie Chen, Nadia Magnenat-Thalmann

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

21 Citations (Scopus)

Abstract

Motion capture is widely used in sports, entertainment and medical applications. An important issue is to recover motion capture data that has been corrupted by noise and missing data entries during acquisition. In this paper, we propose a new method to recover corrupted motion capture data through trajectory-based sparse representation. The data is firstly represented as trajectories with fixed length and high correlation. Then, based on the sparse representation theory, the original trajectories can be recovered by solving the sparse representation of the incomplete trajectories through the OMP algorithm using a dictionary learned by K-SVD. Experimental results show that the proposed algorithm achieves much better performance, especially when significant portions of data is missing, than the existing algorithms.

Original languageEnglish
Title of host publication2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings
PublisherIEEE Computer Society
Pages709-713
Number of pages5
ISBN (Print)9781479923410
DOIs
Publication statusPublished - Sep 2013
Externally publishedYes
Event2013 20th IEEE International Conference on Image Processing, ICIP 2013 - Melbourne, VIC, Australia
Duration: 15 Sep 201318 Sep 2013

Publication series

Name2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings

Conference

Conference2013 20th IEEE International Conference on Image Processing, ICIP 2013
Country/TerritoryAustralia
CityMelbourne, VIC
Period15/09/1318/09/13

Keywords

  • completing
  • K-SVD
  • Motion capture
  • sparse representation
  • trajectory

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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