PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Structure inference for Bayesian multisensory perception and tracking
Timothy Hospedales, Joel Cartwright and Sethu Vijayakumar
In: Proc. International Joint Conference on Artificial Intelligence (IJCAI '07) (2007) IJCAI , pp. 2122-2128.


We investigate a solution to the problem of multi-sensor 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.

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EPrint Type:Book Section
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
Multimodal Integration
ID Code:3398
Deposited By:Timothy Hospedales
Deposited On:10 February 2008