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Moving point target detection using temporal variance filter in IR imagery

A crucial problem in Infrared Search and Track (IRST) systems is the detection of moving point targets, there are many algorithms reported in the literature for dealing with this problem, yet none of them yield acceptable results under all situations. In this paper, we describe a new temporal variance filter (TVF) for detecting targets whose velocity are higher than 1 pixel/frame; the filter iteratively estimates the temporal variance of each pixel, then subtracts the last iteration step variance from the variance of current step. Subsequently, we introduce a novel image segmentation algorithm in order to extract point targets from clutter background, the trajectories of the point targets could be established by post-processing algorithm. Before applying the temporal filter, the anti max-median filter given by Suyog D. Deshpande et al. is incorporated as a preprocessing technique to suppress cloud clutter. It is assumed that targets' velocity is higher than 1 pixel/frame; targets with sub-pixel/frame velocity are not considered. The performance of our approach is evaluated by using available real-world infrared image sequences containing simulated moving point targets; it performs steadily under most situations.