Automatic multi-target recognition from two classes using quadratic correlation filters

Andres Rodriguez; B.V. K. Vijaya Kumar

doi:10.1117/12.852716

12 May 2010 Automatic multi-target recognition from two classes using quadratic correlation filters

Proceedings Volume 7696, Automatic Target Recognition XX; Acquisition, Tracking, Pointing, and Laser Systems Technologies XXIV; and Optical Pattern Recognition XXI; 76960C (2010) https://doi.org/10.1117/12.852716
Event: SPIE Defense, Security, and Sensing, 2010, Orlando, Florida, United States

Abstract

Correlation filters (CFs) can detect multiple targets in one scene making them well-suited for automatic target recognition (ATR) applications. Quadratic CFs (QCFs) can improve performance over linear CFs. QCFs are able to detect one class of targets and reject clutter. We present a method to increase the QCF capabilities to detect two classes of targets and reject clutter. We integrate the ATR tasks of detection, recognition, and tracking algorithms using the Multi-Frame Correlation Filter (MFCF) framework. Our simulation results demonstrate the algorithm's ability to detect multiple targets from two classes while rejecting clutter.

Citation Download Citation

Andres Rodriguez and B.V. K. Vijaya Kumar "Automatic multi-target recognition from two classes using quadratic correlation filters", Proc. SPIE 7696, Automatic Target Recognition XX; Acquisition, Tracking, Pointing, and Laser Systems Technologies XXIV; and Optical Pattern Recognition XXI, 76960C (12 May 2010); https://doi.org/10.1117/12.852716

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