Interpreting a dynamic and uncertain world: task-based control

doi:10.1016/S0004-3702(98)00004-6

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Interpreting a dynamic and uncertain world: task-based control

1998-04-01

Richard J. Howarth

DOI：10.1016/S0004-3702(98)00004-6

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Abstract

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In this paper we show that it can be beneficial to have a high-level vision component that guides the reasoning of the whole vision system when interpreting a dynamic and uncertain world. This guidance is provided by an attentional mechanism that exploits knowledge of the specific problem being solved. Here we develop a general framework for such an attentional mechanism and its application to understanding dynamic scenes. This attentional mechanism can enable a vision system to perform a given domain task while expending minimal resources. We have developed a component that uses Bayesian networks combined with a deictic representation to select what, when and how to use processed data from a fixed camera. We apply two forms of Bayesian network, which (1) create a dynamic structure to reflect the spatial organisation of the data and (2) measure task relatedness. Together these give attentional focus making the reasoning performed relevant to the task. (C) 1998 Elsevier Science B.V.

Keywords:

high-level computer vision

surveillance

attention

event reasoning

visual behaviour

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