Human motion capture data recovery via trajectory-based sparse representation

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

19 Scopus Citations
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Author(s)

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

Detail(s)

Original languageEnglish
Title of host publication2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings
Pages709-713
Publication statusPublished - 2013
Externally publishedYes

Conference

Title2013 20th IEEE International Conference on Image Processing, ICIP 2013
LocationMelbourne Convention and Exhibition Centre (MCEC)
PlaceAustralia
CityMelbourne, VIC
Period15 - 18 September 2013

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. © 2013 IEEE.

Research Area(s)

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

Citation Format(s)

Human motion capture data recovery via trajectory-based sparse representation. / Hou, Junhui; Chau, Lap-Pui; He, Ying; Chen, Jie; Magnenat-Thalmann, Nadia.

2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings. 2013. p. 709-713 6738146.

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review