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Personal and Ubiquitous Computing

, Volume 13, Issue 1, pp 3–14 | Cite as

Robust multimodal audio–visual processing for advanced context awareness in smart spaces

  • A. PnevmatikakisEmail author
  • J. Soldatos
  • F. Talantzis
  • L. Polymenakos
Original Article

Abstract

Identifying people and tracking their locations is a key prerequisite to achieving context awareness in smart spaces. Moreover, in realistic context-aware applications, these tasks have to be carried out in a non-obtrusive fashion. In this paper we present a set of robust person-identification and tracking algorithms, based on audio and visual processing. A main characteristic of these algorithms is that they operate on far-field and un-constrained audio–visual streams, which ensure that they are non-intrusive. We also illustrate that the combination of their outputs can lead to composite multimodal tracking components, which are suitable for supporting a broad range of context-aware services. In combining audio–visual processing results, we exploit a context-modeling approach based on a graph of situations. Accordingly, we discuss the implementation of realistic prototype applications that make use of the full range of audio, visual and multimodal algorithms.

Keywords

Linear Discriminant Analysis Gaussian Mixture Model Time Delay Estimation Smart Space Perceptual Component 
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.

Notes

Acknowledgments

This work is sponsored by the European Union under the integrated project CHIL, contract number 506909.

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Copyright information

© Springer-Verlag London Limited 2007

Authors and Affiliations

  • A. Pnevmatikakis
    • 1
    Email author
  • J. Soldatos
    • 1
  • F. Talantzis
    • 1
  • L. Polymenakos
    • 1
  1. 1.Athens Information TechnologyAthensGreece

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