## 1 Introduction  
Object tracking is one of the essential methods for dynamic object observation in computer vision, which has been widely used in video surveillance, automatic navigation, artificial intelligence, and other applications [1]. The purpose of object tracking is to predict the object's size and position in subsequent frames based on the initial frame of a video sequence [2]. With the continuous development of commercial remote sensing satellites, such as the Jilin-1 and Zhuhai-1 satellite constellations, high-resolution videos through video satellites are an affordable method of gazing and observing a specific Earth's area to obtain rich information. The Jilin-1 video satellite was launched by China Chang Guang Satellite Technology Co., Ltd and provided 4k high-resolution imagery, capturing detailed information about an area. Indeed, the satellite imagery was about 1-m resolution at 30 frames per second, and therefore object tracking in such satellite videos has gradually become a new research direction. The corresponding practical applications involve traffic vehicle tracking [3], monitoring seawater [4], monitoring natural disasters [5], military reconnaissance, and precision guidance. Object tracking in a satellite video is widely applied in many fields and explicitly analyzing the object's motion laws according to its trajectory is of great significance. Most moving objects in satellite videos are ships, vehicles, and airplanes, as these are common objects in satellite videos with significant research value. [PERSON] et al. [5] proposed a velocity correlation filter (VCF) algorithm by employing the velocity features with an inertial mechanism (IM) and constructing a specific kernelized correlation filter for object tracking in satellite videos. This method has an appealing performance in single background, but the method's effectiveness in complex backgrounds or similar objects is yet unknown. Moreover, in 2019 the authors further suggested a hybrid kernel correlation filter (HKCF) tracker that adaptively used two complementary features in the ridge regression framework, combined with an adaptive fusion strategy exploiting both features in various videos [6]. Nevertheless, this method is also only suitable for simple background cases, and the optical flow is quite sensitive to illumination variations. In general, the HOG features play a significant role without emphasizing the response of the optical flow features. [PERSON] et al. [7] constructed a robust tracker combining the KCF tracker with a three-frame difference algorithm to overcome the difficulty of similar object interference and fewer object features. This method only considers a single background and does not consider the existence of complex background and object occlusions. Moreover, tracking drift can occur when the object is occluded, resulting in object tracking failure. [PERSON] et al. [8] developed a tracker based on a high-speed correlation filter (CF) for object tracking in satellite videos. This technique utilized the global motion features of the moving object in the satellite videos to constrain the tracking process, which is achieved by applying a Kalman filter (KF) to correct the moving object's tracking trajectory. Although this method has dramatically improved performance and tracking accuracy, the Kalman filter must provide convergence before combining it with the KCF algorithm. Therefore, object tracking in the first few frames still relies on the KCF algorithm, and thus the tracking accuracy does not improve much. [PERSON] et al. [9] solved the object occlusion problem and reduced boundary effect during motion estimation by combining a correlation filter with a Kalman filter. However, in the case of similar objects or complex object backgrounds, the tracking effectiveness of this method needs to be experimentally verified.