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30 September 2013 Multiple target tracking from images using the maximum likelihood HPMHT
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Abstract
In this paper, we address the problem of passive tracking of multiple targets with the help of images obtained from passive infrared (IR) platforms. Conventional approaches to this problem, which involve thresholding, measurement detection, data association and filtering, encounter problems due to target energy being spread across multiple cells of the IR imagery. A histogram based probabilistic multi-hypothesis tracking (H-PMHT) approach provides an automatic means of modeling targets that are spread in multiple cells in the imaging sensor(s) by relaxing the need for hard decisions on measurement detection and data association. Further, we generalize the conventional HPMHT by adding an extra layer of EM iteration that yields the maximum likelihood (ML) estimate of the number of targets. With the help of simulated focal plane array (FPA) images, we demonstrate the applicability of MLHPMHT for enumerating and tracking multiple targets.
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Peter Willett, Balakumar Balasingam, Darin Dunham, and Terry Ogle "Multiple target tracking from images using the maximum likelihood HPMHT", Proc. SPIE 8857, Signal and Data Processing of Small Targets 2013, 88570L (30 September 2013); https://doi.org/10.1117/12.2027297
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