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Summary: An Adaptive Fusion Architecture for Target Tracking
Gareth Loy, Luke Fletcher, Nicholas Apostoloff and Alexander Zelinsky
Department of Systems Engineering
Research School of Information Sciences and Engineering
Australian National University, Canberra 0200
{gareth, luke, nema, alex}@syseng.anu.edu.au
Abstract
A vision system is demonstrated that adaptively allocates
computational resources over multiple cues to robustly
track a target in 3D. The system uses a particle filter
to maintain multiple hypotheses of the target location.
Bayesian probability theory provides the framework for
sensor fusion, and resource scheduling is used to intelli-
gently allocate the limited computational resources avail-
able across the suite of cues. The system is shown to track
a person in 3D space moving in a cluttered environment.
1 Introduction
Visually acquiring and tracking targets is a key problem
in computer vision, and new and innovative techniques are
constantly being developed. However, despite the impres-
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