Using Hidden Markov Models for Recognizing Action Primitives in Complex Actions

  • Volker Krüger
  • Daniel Grest
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4522)

Abstract

There is biological evidence that human actions are composed out of action primitives, like words and sentences being composed out of phonemes. Similarly to language processing, one possibility to model and recognize complex actions is to use grammars with action primitives as the alphabet. A major challenge here is that the action primitives need to be recovered first from the noisy input signal before further processing with the action grammar can be done. In this paper we combine a Hidden Markov Model-based approach with a simplified version of a condensation algorithm which allows to recover the action primitives in an observed action. In our approach, the primitives may have different lengths, no clear “divider” between the primitives is necessary. The primitive detection is done online, no storing of past data is required. We verify our approach on a large database. Recognition rates are slightly lower than the rate when recognizing the singular action primitives.

Keywords

Hide Markov Model Speech Recognition Humanoid Robot Motion Primitive Action Primitive 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Volker Krüger
    • 1
  • Daniel Grest
    • 1
  1. 1.Computer Vision and Machine Intelligence Lab, CIT, Aalborg University, Lautrupvang 15, 2750 BallerupDenmark

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