Scale-independent spatio-temporal statistical shape representations for 3D human action recognition

Marco Körner, Daniel Haase, Joachim Denzler

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

1 Scopus citations

Abstract

Since depth measuring devices for real-world scenarios became available in the recent past, the use of 3d data now comes more in focus of human action recognition. We propose a scheme for representing human actions in 3d, which is designed to be invariant with respect to the actor's scale, rotation, and translation. Our approach employs Principal Component Analysis (PCA) as an exemplary technique from the domain of manifold learning. To distinguish actions regarding their execution speed, we include temporal information into our modeling scheme. Experiments performed on the CMU Motion Capture dataset shows promising recognition rates as well as its robustness with respect to noise and incorrect detection of landmarks.

Original languageEnglish
Title of host publicationICPRAM 2012 - Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods
Pages288-294
Number of pages7
StatePublished - 2012
Externally publishedYes
Event1st International Conference on Pattern Recognition Applications and Methods, ICPRAM 2012 - Vilamoura, Algarve, Portugal
Duration: 6 Feb 20128 Feb 2012

Publication series

NameICPRAM 2012 - Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods
Volume1

Conference

Conference1st International Conference on Pattern Recognition Applications and Methods, ICPRAM 2012
Country/TerritoryPortugal
CityVilamoura, Algarve
Period6/02/128/02/12

Keywords

  • Human action recognition
  • Manifold learning
  • PCA
  • Shape model

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