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Proceedings Paper

Improved training for target detection using Fukunaga-Koontz transform and distance classifier correlation filter
Author(s): M. I. Elbakary; M. S. Alam; M. S. Aslan
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Paper Abstract

In a FLIR image sequence, a target may disappear permanently or may reappear after some frames and crucial information such as direction, position and size related to the target are lost. If the target reappears at a later frame, it may not be tracked again because the 3D orientation, size and location of the target might be changed. To obtain information about the target before disappearing and to detect the target after reappearing, distance classifier correlation filter (DCCF) is trained manualy by selecting a number of chips randomly. This paper introduces a novel idea to eliminates the manual intervention in training phase of DCCF. Instead of selecting the training chips manually and selecting the number of the training chips randomly, we adopted the K-means algorithm to cluster the training frames and based on the number of clusters we select the training chips such that a training chip for each cluster. To detect and track the target after reappearing in the field-ofview ,TBF and DCCF are employed. The contduced experiemnts using real FLIR sequences show results similar to the traditional agorithm but eleminating the manual intervention is the advantage of the proposed algorithm.

Paper Details

Date Published: 17 March 2008
PDF: 12 pages
Proc. SPIE 6977, Optical Pattern Recognition XIX, 69770F (17 March 2008); doi: 10.1117/12.778370
Show Author Affiliations
M. I. Elbakary, Univ. of South Alabama (United States)
M. S. Alam, Univ. of South Alabama (United States)
M. S. Aslan, Univ. of South Alabama (United States)

Published in SPIE Proceedings Vol. 6977:
Optical Pattern Recognition XIX
David P. Casasent; Tien-Hsin Chao, Editor(s)

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