Empirical comparison of two least-squares methods for computing image shift with application to correlation tracking

John E. Albus; Phillip Hoang

doi:10.1117/12.547793

27 July 2004 Empirical comparison of two least-squares methods for computing image shift with application to correlation tracking

John E. Albus, Phillip Hoang

Author Affiliations +

Proceedings Volume 5430, Acquisition, Tracking, and Pointing XVIII; (2004) https://doi.org/10.1117/12.547793
Event: Defense and Security, 2004, Orlando, Florida, United States

Abstract

Many trackers make use of correlation techniques to provide an estimate of the shift between incoming imagery and a stored reference image. One efficient method for estimating this shift is based on a least squares approach that makes use of gradient and difference imagery to avoid the computationally expensive construction of a correlation surface. A problem with this method is that it tends to underestimate image shifts when there is significant noise in the reference image-which is often the case. An alternative method makes use of a generalized least squares approach that takes the noise in the reference image into account when estimating the image shift. This paper describes these two correlation algorithms and presents the results of an empirical comparison of their performance under varying noise conditions for a variety of test imagery.

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John E. Albus and Phillip Hoang "Empirical comparison of two least-squares methods for computing image shift with application to correlation tracking", Proc. SPIE 5430, Acquisition, Tracking, and Pointing XVIII, (27 July 2004); https://doi.org/10.1117/12.547793

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