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Developing Context Sensitive HMM Gesture Recognition

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Gesture-Based Communication in Human-Computer Interaction (GW 2003)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2915))

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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.

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© 2004 Springer-Verlag Berlin Heidelberg

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Sage, K., Howell, A.J., Buxton, H. (2004). Developing Context Sensitive HMM Gesture Recognition. In: Camurri, A., Volpe, G. (eds) Gesture-Based Communication in Human-Computer Interaction. GW 2003. Lecture Notes in Computer Science(), vol 2915. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24598-8_26

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  • DOI: https://doi.org/10.1007/978-3-540-24598-8_26

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-21072-6

  • Online ISBN: 978-3-540-24598-8

  • eBook Packages: Springer Book Archive

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