Infrared Moving Small-Target Detection via Spatiotemporal Consistency of Trajectory Points

Abstract

Effective detection of infrared (IR) moving small targets in complex cluttered environments plays a key role in IR search and track systems for self-defense or attacks. In this letter, an IR moving small-target detection algorithm utilizing a spatiotemporal consistency of motion trajectories is proposed. First, feature points are densely sampled and tracked using the dense optical flow algorithm to compute dense trajectories. Second, suspected trajectories are deleted by utilizing the moving characteristics of the target. Third, under the assumption that each small target is defined as a compact space region, a binary image is created depending on the image coordinates of the trajectory points, from which salient contours are extracted as candidate target regions. Finally, a coding mechanism for contour numbering is introduced, and the moving targets are distinguished from the backgrounds by the temporal consistency of contour codewords. Several experiments were conducted, and their results demonstrate that our proposed method can detect small moving IR targets with higher detection rate, lower false alarm rate, and less running time compared with the state-of-the-art methods.