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Developing Context Sensitive HMM Gesture Recognition

  • Kingsley Sage
  • A. Jonathan Howell
  • Hilary Buxton
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2915)

Abstract

We are interested in methods for building cognitive vision systems to understand activities of expert operators for our ActIPret System. Our approach to the gesture recognition required here is to learn the generic models and develop methods for contextual bias of the visual interpretation in the online system. The paper first introduces issues in the development of such flexible and robust gesture learning and recognition, with a brief discussion of related research. Second, the computational model for the Hidden Markov Model (HMM) is described and results with varying amounts of noise in the training and testing phases are given. Third, extensions of this work to allow both top-down bias in the contextual processing and bottom-up augmentation by moment to moment observation of the hand trajectory are described.

Keywords

Root Mean Square Hide Markov Model Gesture Recognition Hide State Trajectory Data 
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-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Kingsley Sage
    • 1
  • A. Jonathan Howell
    • 1
  • Hilary Buxton
    • 1
  1. 1.School of Cognitive and Computing SciencesUniversity of SussexBrightonUK

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