View-invariant modeling and recognition of human actions using grammars

  • Authors:

  • Abhijit S. Ogale;Alap Karapurkar;Yiannis Aloimonos

  • Affiliations:

  • Computer Vision Laboratory, Dept. of Computer Science, University of Maryland, College Park, MD;Computer Vision Laboratory, Dept. of Computer Science, University of Maryland, College Park, MD;Computer Vision Laboratory, Dept. of Computer Science, University of Maryland, College Park, MD

  • Venue:
  • WDV'05/WDV'06/ICCV'05/ECCV'06 Proceedings of the 2005/2006 international conference on Dynamical vision
  • Year:
  • 2006

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Abstract

In this paper, we represent human actions as sentences generated by a language built on atomic body poses or phonemes. The knowledge of body pose is stored only implicitly as a set of silhouettes seen from multiple viewpoints; no explicit 3D poses or body models are used, and individual body parts are not identified. Actions and their constituent atomic poses are extracted from a set of multiview multiperson video sequences by an automatic key frame selection process, and are used to automatically construct a probabilistic context-free grammar (PCFG), which encodes the syntax of the actions. Given a new single viewpoint video, we can parse it to recognize actions and changes in viewpoint simultaneously. Experimental results are provided.