## 1 Introduction
However, objects in satellite videos are different from traditional objects, and tracking them is challenging, e.g., the object of interest comprises only a few pixels, and therefore similar objects raise the tracking difficulty. Moreover, objects occluded by clouds or other buildings impose tracking drifts. Hence, primarily, the algorithm must overcome object occlusion (Figure 1). Recently, researchers have introduced the idea of correlation filtering into the object tracking algorithm. The earliest minimum output sum of squared error (MOSSE) [10] algorithm utilized the most basic correlation filtering idea. The circulant structure kernel (CSK) [11] correlation filter algorithm modifies MOSSE, while the kernelized correlation filter (KCF) [12] algorithm combines the advantages of MOSSE and CSK to improve the performance further. This kind of algorithm [13; 14; 15; 16; 17] reduces time complexity through a fast  
Figure 1: Satellite videos. (**a**) small object size is about 10 \(\times\) 12 pixels. (**b**) the moving object is occluded. (**c**) the object is similar to its background. Fourier transform and utilizes cyclic shift samples for sufficient training to enhance object tracking speed and accuracy. The C-COT [18] algorithm utilizes the deep neural network VGG-Net to extract features, interpolates feature maps of different resolutions into the continuous space domain utilizing cubic interpolation, and then finds the object position with subpixel accuracy employing the Hessian matrix. The ECO [19] algorithm improves C-COT and attains an appealing object tracking by reducing the model parameters through feature-subset dimensionality reduction, merging similar sample sets, and sparse updating strategies. The innovative improvement of the ECO algorithm substantially enhances the tracking effect. The correlation filter algorithms belong to discriminative object tracking methods, which have recently become a mainstream research direction due to their high speed and accuracy [20; 21; 22; 23; 24; 25]. Since an object's deformation in satellite videos is not apparent, it can be considered that there is almost no deformation, and thus we utilize the KCF algorithm to track the moving object in the satellite videos. However, simply applying the KCF algorithm presents the following shortcomings. First, when the object is occluded, tracking drifts, leading to tracking failure. Second, utilizing dense sampling and Fourier transform causes the tracker to produce boundary effects, affecting tracking accuracy. Third, similar objects may be regarded as real objects, affecting object tracking. This paper proposes multi-feature fusion and motion trajectory compensation methods to solve poor tracking accuracy. Specifically, we build a robust tracker that fuses the object's various response features, improves object location accuracy through subpixel positioning, and relies on the adaptive Kalman filter to correct the tracking results of the correlation filter. More precisely, the main contributions of this paper are as follows:  
(1) We propose a multi-feature fusion method to enhance the object features' expressiveness and improve object tracking in satellite videos. The features employed are the histogram of oriented gradient (HOG) and the convolutional neural networks (CNN) features. (2) A quadratic parabolic model is proposed to fit the discrete object response values. This scheme approximates the discrete response map to a continuous response map, and based on this, a Taylor series method is used to obtain subpixel position accuracy. This strategy solves the subpixel localization accuracy problem of moving objects. (3) We reduce the estimation error caused by the noise covariance in the randomly selected Kalman filter (KF) by proposing an adaptive adjustment of both covariances using the state discriminant method (SDM) that affords an increased convergence speed to correct the tracking results of the correlation filter quickly.