18 May 2006 Improved target detection algorithm using Fukunaga-Koontz transform and distance classifier correlation filter
Author Affiliations +
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.
© (2006) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
A. Bal, M. S. Alam, M. S. Aslan, "Improved target detection algorithm using Fukunaga-Koontz transform and distance classifier correlation filter", Proc. SPIE 6234, Automatic Target Recognition XVI, 62340H (18 May 2006); doi: 10.1117/12.666198; https://doi.org/10.1117/12.666198
PROCEEDINGS
12 PAGES


SHARE
Back to Top