Adaptive Automatic Target Recognition with SVM Boosting for Outlier Detection

  • Kieron Messer
  • Josef Kittler
  • John Haddon
  • Graham Watson
  • Sharon Watson
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1876)

Abstract

This paper is concerned with the detection of dim targets in cluttered image sequences. It is an extension of our previous work [7] in which we viewed target detection as an outlier detection problem. In that work the background was modelled by a uni-modal Gaussian. In this paper a Gaussian mixture-model is used to describe the background in which the the number of components is automatically selected. As an outlier does not automatically imply a target, a final stage has been added in which all points below a set density function value are passed to a support vector classifier to be identified as a target or background. This system is compared favourably to a baseline technique [12].

Keywords

Automatic target recognition Mixture Modelling Support Vector Machines Outlier Detection 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Kieron Messer
    • 1
  • Josef Kittler
    • 1
  • John Haddon
    • 2
  • Graham Watson
    • 2
  • Sharon Watson
    • 2
  1. 1.University of SurreyGuildfordUK
  2. 2.Defence Evaluation and Research AgencyFarnboroughUK

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