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