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Automatic Target Recognition and Tracking in Forward-Looking Infrared Image Sequences with a Complex Background

Seok Pil Yoon, Taek Lyul Song, and Tae Han Kim*
International Journal of Control, Automation, and Systems, vol. 11, no. 1, pp.21-32, 2013

Abstract : This paper presents a technique for automatic airborne target recognition and tracking in forward-looking infrared (FLIR) images with a complex background. An image splitting and merging method is applied for detecting target signals. The presence of a complex background due to clouds and sun glint generates clutter in the image with the resulting possibility of false alarms. A Bayesian classifier trained using the NMI (normalized moment of inertia) feature is proposed for efficient clutter rejection. After classification, target candidates are entered into a tracking filter. As an efficient and robust multi-target tracking filter in cluttered environments, the JDC-JIHPDAF is proposed. Experimental results using a wide range of real FLIR images ensure reliable classification and automatic target recognition performance.

Keyword : Automatic target recognition, Bayesian classifier, JDC-JIHPDAF, NMI features.

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