Structure inference for Bayesian multisensory perception and tracking

Authors:
Timothy M. Hospedales;Joel J. Cartwright;Sethu Vijayakumar
Affiliations:
School of Informatics, University of Edinburgh, Scotland, UK;School of Informatics, University of Edinburgh, Scotland, UK;School of Informatics, University of Edinburgh, Scotland, UK
Venue:
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Year:
2007

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Abstract

We investigate a solution to the problem of multisensor perception and tracking by formulating it in the framework of Bayesian model selection. Humans robustly associate multi-sensory data as appropriate, but previous theoretical work has focused largely on purely integrative cases, leaving segregation unaccounted for and unexploited by machine perception systems. We illustrate a unifying, Bayesian solution to multi-sensor perception and tracking which accounts for both integration and segregation by explicit probabilistic reasoning about data association in a temporal context. Unsupervised learning of such a model with EM is illustrated for a real world audio-visual application.