Abstract
In this chapter, we review the recent trends and advancements on pattern recognition and tracking in forward looking infrared (FLIR) imagery. In particular, we discuss several target detection and tracking algorithms for single/multiple target detection and tracking purposes. Each detection and tracking algorithm utilizes various properties of targets and image frames of a given sequence. At first we discuss a Fukunga–Kuntz Transform and template matching based algorithm for target detection and tracking. Then, we described a novel algorithm for target detection and tracking using fringe-adjusted joint transform correlation (JTC) and template matching. Finally, we discussed an invariant detection and tracking algorithm using a combination of fringe-adjusted JTC and a composited weighted reference function. The impact of sensor ego motion and possible compensation techniques as well as the role of image segmentation towards enhancing the accuracy of target detection and tracking is also described. The aforementioned techniques can detect and track small objects comprising of only a few pixels and is capable of compensating the high ego-motion of the sensor for various challenging scenarios. Test results obtained using real life FLIR image sequences are included to verify the effectiveness of the above mentioned algorithms for target detection and tracking in FLIR imagery.
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