Structure Inference for Bayesian Multisensory Perception and Tracking

Timothy M Hospedales, Joel J Cartwright, Sethu Vijayakumar

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.

Subjects: 19.1 Perception; 3.4 Probabilistic Reasoning

Submitted: Oct 13, 2006


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