---
title: "Structure Inference for Bayesian Multisensory Perception and Tracking"
authors:
  - "Timothy M. Hospedales"
  - "Joel J. Cartwright"
  - "Sethu Vijayakumar"
doi: ""
url: "https://ijcai.org/proceedings/2007/342"
pdf: "https://ijcai.org/proceedings/2007/0342.pdf"
venue: "Proceedings of the Twentieth International Joint Conference on Artificial Intelligence"
year: 2007
pages: "2122-2127"
track: ""
keywords:
---

# Structure Inference for Bayesian Multisensory Perception and Tracking

Timothy M. Hospedales, Joel J. Cartwright, Sethu Vijayakumar

*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.