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

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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.

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Acknowledgments

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

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Authors and Affiliations

  1. Athens Information Technology, 19.5 km Markopoulou Peania, Ave., Athens, Greece

    A. Pnevmatikakis, J. Soldatos, F. Talantzis & L. Polymenakos

Authors
  1. A. Pnevmatikakis
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  2. J. Soldatos
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  3. F. Talantzis
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  4. L. Polymenakos
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Correspondence to A. Pnevmatikakis.

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Pnevmatikakis, A., Soldatos, J., Talantzis, F. et al. Robust multimodal audio–visual processing for advanced context awareness in smart spaces. Pers Ubiquit Comput 13, 3–14 (2009). https://doi.org/10.1007/s00779-007-0169-9

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  • DOI: https://doi.org/10.1007/s00779-007-0169-9

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