## 1 Introduction
Many background models have been proposed, such as the basic models, background estimation, background clustering, subspace learning, kernel density estimation, Gaussian mixture model (GMM), and visual background extractor (ViBe). These methods provide a vital basis for moving vehicle detection. Influenced by a dynamic background and parallax, the ideal results are often not produced when directly using these methods. Either the precision is too low, or the false alarm rate is too high. Researchers are aware of this limitation, and have proposed many improved methods. [PERSON] et al. studied moving vehicle detection from satellite videos based on a median background [9]. [PERSON] et al. proposed a local regional approach with error-bounded foreground and background modeling for moving vehicle detection in satellite videos [10; 11; 12]. [PERSON] et al. adopted accumulated trajectory to help in moving vehicle detection [13]. [PERSON] et al. proposed an algorithm based on local noise modeling, which differentiates potential vehicle targets from noise patterns by an exponential probability distribution [5]. [PERSON] et al. developed an improved Gaussian-based background subtractor algorithm and a normalized frame differencing labeling method for moving objects' detection from satellite videos [14]. 2. Moving vehicles tracking methods Moving object tracking is a problem whereby associating detected objects form trajectories [15]. According to the number of tracking objects, it is commonly classified into single-object tracking (SOT) and multi-object tracking (MOT). According to how objects are initialized, it is divided into Detection-Based Tracking (DBT) and Detection-Free Tracking (DFT) [16]. According to object matching methods, it can be categorized into generative and discriminative algorithms. Generative algorithms focus on building a model for the target in the current frame, and then searching potential regions that are similar to the target in the next frame. Discriminative algorithms work as a binary classifier, which takes the target as the positive sample and the background as the negative sample in the current frame. The trained classifier will be used to search the optimal regions in the next frame. [PERSON] et al. proposed a simple online and real-time tracking framework SORT based on the Kalman filter and Hungary algorithm [17]. [PERSON] et al. developed SORT with a deep association metric, Deep SORT [18]. [PERSON] et al. proposed a robust tracking approach based on the correlation filter. The Kalman filter models the motion state of the target and helps to correct the deviations of the tracking trajectory [19]. [PERSON] et al. proposed a velocity correlation filter algorithm, in which a velocity feature obtained from optical flow and an inertia mechanism were used to construct a specific kernel correlation filter for object tracking [20]. [PERSON] et al. proposed a tracking algorithm based on kernelized correlation filters embedded with motion estimations [1]. [PERSON] et al. used a simple regression network to combine a regression model exploiting the abundant background context to develop a robust tracker with convolutional layers [21]. Both appearance features and motion features were extracted by pretrained deep neural networks, and used for accurate object tracking. [PERSON] et al. used a high-resolution Siamese Network to track moving objects in a satellite video [22]. Although a lot of research has been conducted facing the problem of obscuring, most tracking frameworks adopt re-identification (ReID) for long-range feature level association [23], but none of them have paid attention to how to recover the obscured vehicles. In contrast, our method repairs broken trajectories and estimates obscured vehicles. This is of great importance for observing obscured areas. Despite the progress made in the application of video satellite technology in the estimation of traffic flow parameters, the accurate prediction of traffic flow parameters remains crucial for traffic management.