Abstract
In this paper, we review the recent trends and advancements on correlation-based pattern recognition and tracking in forward-looking infrared (FLIR) imagery. In particular, we discuss matched filter-based correlation techniques for target detection and tracking which are widely used for various real time applications. We analyze and present test results involving recently reported matched filters such as the maximum average correlation height (MACH) filter and its variants, and distance classifier correlation filter (DCCF) and its variants. Test results are presented for both single/multiple target detection and tracking using various real-life FLIR image sequences.
Highlights
Pattern recognition deals with the detection and identification of a desired pattern or target in an unknown input scene, which may or may not contain the target, and the determination of the spatial location of any target present
This paper presents some widely used pattern recognition and target tracking techniques adopted for Forward-looking infrared (FLIR) imagery
This image database has a total of 50 real life infrared video sequences, some of which contain a single target in the scene and some contain multiple targets
Summary
Pattern recognition deals with the detection and identification of a desired pattern or target in an unknown input scene, which may or may not contain the target, and the determination of the spatial location of any target present. The thermal images are obtained by sensing the radiation in the infrared spectrum, which is either emitted or reflected by the object in the scene Due to this property the images obtained from an infrared sensor have extremely low SNR, which results in limited information for performing detection or tracking task. We discuss several target detection and tracking algorithms which are based on the recently reported matched filter-based correlation techniques such as the MACH, EMACH, DCCF, and PDCCF filters
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