Structure Inference for Bayesian Multisensory Perception and Tracking
Proceedings of the Twentieth International Joint Conference on Artificial Intelligence. Pages 2122-2127.
Abstract
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.
@inproceedings{ijcai2007p342,
title = {Structure Inference for Bayesian Multisensory Perception and Tracking},
author = {Hospedales, Timothy M. and Cartwright, Joel J. and Vijayakumar, Sethu},
booktitle = {Proceedings of the Twentieth International Joint Conference on
Artificial Intelligence, {IJCAI-07}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Ramon López de Mántaras},
pages = {2122--2127},
year = {2007},
url = {https://ijcai.org/proceedings/2007/342},
}
TY - CPAPER TI - Structure Inference for Bayesian Multisensory Perception and Tracking AU - Hospedales, Timothy M. AU - Cartwright, Joel J. AU - Vijayakumar, Sethu T2 - Proceedings of the Twentieth International Joint Conference on Artificial Intelligence, IJCAI-07 PB - International Joint Conferences on Artificial Intelligence PY - 2007 SP - 2122 EP - 2127 UR - https://ijcai.org/proceedings/2007/342 ER -Copied
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