Structure Inference for Bayesian Multisensory Perception and Tracking
International Joint Conference on Artificial Intelligence (IJCAI 2007)
View/ Open
Date
01/2007Author
Hospedales, Timothy
Cartwright, Joel
Metadata
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.