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Proceedings Paper

Improved target detection algorithm using Fukunaga-Koontz transform and distance classifier correlation filter
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Paper Abstract

Often sensor ego-motion or fast target movement causes the target to temporarily go out of the field-of-view leading to reappearing target detection problem in target tracking applications. Since the target goes out of the current frame and reenters at a later frame, the reentering location and variations in rotation, scale, and other 3D orientations of the target are not known thus complicating the detection algorithm has been developed using Fukunaga-Koontz Transform (FKT) and distance classifier correlation filter (DCCF). The detection algorithm uses target and background information, extracted from training samples, to detect possible candidate target images. The detected candidate target images are then introduced into the second algorithm, DCCF, called clutter rejection module, to determine the target coordinates are detected and tracking algorithm is initiated. The performance of the proposed FKT-DCCF based target detection algorithm has been tested using real-world forward looking infrared (FLIR) video sequences.

Paper Details

Date Published: 18 May 2006
PDF: 12 pages
Proc. SPIE 6234, Automatic Target Recognition XVI, 62340H (18 May 2006); doi: 10.1117/12.666198
Show Author Affiliations
A. Bal, 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. 6234:
Automatic Target Recognition XVI
Firooz A. Sadjadi, Editor(s)

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