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

A. Bal; M. S. Alam; M. S. Aslan

doi:10.1117/12.666198

18 May 2006 Improved target detection algorithm using Fukunaga-Koontz transform and distance classifier correlation filter

A. Bal, M. S. Alam, M. S. Aslan

Author Affiliations +

Proceedings Volume 6234, Automatic Target Recognition XVI; 62340H (2006) https://doi.org/10.1117/12.666198
Event: Defense and Security Symposium, 2006, Orlando (Kissimmee), Florida, United States

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.

Citation Download Citation

A. Bal, M. S. Alam, and 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); https://doi.org/10.1117/12.666198

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