Improved clutter rejection in automatic target recognition and tracking using eigen-extended maximum average correlation height (EEMACH) filter and polynomial distance classifier correlation filter (PDCCF)

M. F. Islam; M. S. Alam

doi:10.1117/12.666256

17 April 2006 Improved clutter rejection in automatic target recognition and tracking using eigen-extended maximum average correlation height (EEMACH) filter and polynomial distance classifier correlation filter (PDCCF)

M. F. Islam, M. S. Alam

Author Affiliations +

Proceedings Volume 6245, Optical Pattern Recognition XVII; 62450B (2006) https://doi.org/10.1117/12.666256
Event: Defense and Security Symposium, 2006, Orlando (Kissimmee), Florida, United States

Abstract

Various correlation based techniques for detection and classification of targets in forward looking infrared (FLIR) imagery have been developed in last two decades. Correlation filters are attractive for automatic target recognition (ATR) because of their distortion tolerance and shift invariance capabilities. The extended maximum average correlation height (EMACH) filter was developed to detect a target with low false alarm rate while providing good distortion tolerance using a trade off parameter (beta). By decomposing the EMACH filter using the eigen-analysis, another generalized filter, called the eigen-EMACH (EEMACH) filter was developed. The EEMACH filter exhibits consistent performance over a wide range which controls the trade-off between distortion tolerance and clutter rejection. In this paper, a new technique is proposed to combine the EEMACH and polynomial distance classifier correlation filter (PDCCF) for detecting and tracking both single and multiple targets in real life FLIR sequences. At first, EEMACH filter was used to select regions of interest (ROI) from input images and then PDCCF is applied to identify targets using thresholds and distance measures. Both the EEMACH and PDCCF filters are trained with different sizes and orientations corresponding to the target to be detected. This method provides improved clutter rejection capability by exploiting the eigen vectors of the desired class. Both single and multiple targets were identified in each frame by independently using EEMACH-PDCCF algorithm to avoid target disappearance problems under complicated scenarios.

Citation Download Citation

M. F. Islam and M. S. Alam "Improved clutter rejection in automatic target recognition and tracking using eigen-extended maximum average correlation height (EEMACH) filter and polynomial distance classifier correlation filter (PDCCF)", Proc. SPIE 6245, Optical Pattern Recognition XVII, 62450B (17 April 2006); https://doi.org/10.1117/12.666256

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