“…Many different approaches have been proposed for infrared object tracking such as saliency extraction [9], multiscale patch-based contrast measure and a temporal variance filter [14], feature learning and fusion, reliability weight estimation based on nonnegative matrix factorization [15], Poisson reconstruction and the Dempster-Shafer theory [16], three-dimensional scalar field [17], a double-layer region proposal network (RPN) [18], Siamese convolution network [19], a mixture of Gaussians with modified flux density [20], spatial-temporal total variation regularization and weighted tensor [21], two-stage U-skip context aggregation network [22], histogram similarity map based on the Epanechnikov kernel function [23], quaternion discrete cosine transform [24], non-convex optimization [25], Mexican-hat distribution of pixels [26], and Schatten regularization with reweighted sparse enhancement [27].…”