###### Abstract
[PERSON], et al. [6] modeled the motion information of the original frame using the frame-difference method [10]. Traditional methods utilize logic algorithms based on a predetermined set of fixed parameters to distinguish the targets from the background. These fixed parameters are often determined through manual configuration, guided by human expertise, and tailored to specific scenes. However, in scenarios with substantial variations, the reliance on fixed parameters often leads to inconsistent tracking performance. Additionally, both slight camera platform jitter and rapidly changing ground scenes may introduce subtle yet widespread background noise. Traditional methods, including background modeling algorithms like ViBE, often exhibit limited robustness when dealing with such background noise and require additional post-processing. In recent years, the emergence of large video satellite tracking datasets such as VISO [14], AIR-MOT [15], and SAT-MTB [16] has greatly advanced the development of deep learning-based (DL) MOT methods. Various tracking methods proposed new insights in terms of feature enhancement, association logic, trajectory recovery, and multi-task training strategies. Advanced online MOT methods can currently be classified into two-stage tracking-by-detection (TBD) and one-stage joint detection and tracking (JDT) [17]. Figure 3 illustrates the general architecture sketch of both the TBD and JDT methods. The TBD paradigm associates the detection results from an independent detector with trajectories using various similarity metrics. In recent years, deep learning-based object detectors [18; 19] have been rapidly developed, leading to significant improvements in the TBD paradigm. [PERSON], et al. [20] used YOLO v2 [21] to obtain aircraft heads, tails, and full bodies and generate heading state parameter updates of the classic SORT [22] algorithm using the Karman filter [23]. Similarly, [PERSON], et al. [24] achieved the real-time tracking of multiple moving ships using three consecutive frames as an input to the YOLO v3 [25] network. [PERSON], et al. [26] proposed the DSFNet, which uses lightweight 3D convolution to extract dynamic cues from multiple frames. [PERSON], et al. [27] designed the D-RGB network based on the two-stage F-RCNN [28] model. The network subtracts the features from adjacent frames to extract the ROIs for second-stage detection in the RCNN. The joint detection and tracking (JDT) paradigm enables the integration of detection and tracking tasks within a single model. For example, TGraM [15] utilizes graph convolution to update graphs based on extracted features and employs a simplified DANN [29] to balance detection and reidentification (ReID) tasks. Additionally, [PERSON], et al. [30] proposed the CKDNet, a tracking network based on CornerNet [31], to predict the correlation between tracking objects and detection results through a dual-branch LSTM [32] network, and generate matching trajectories employing the Hungarian algorithm [33]. The aforementioned research mainly focuses on enhancing the detection performance by incorporating contextual cues at the feature level. However, this often leads to the redundant extraction of deep features during inference. Additionally, motion-based tracking methods (e.g., SORT [22] and ByteTrack [34]) usually lack the utilization of appearance information. In contrast, some JDT models implicitly extract correlations between objects using graph convolution and LSTM for object association. We suggest enhancing feature reusability to address these challenges, which involves extracting and inheriting deep features from consecutive features to enhance the representation of current features, predicting inter-frame displacement through pixel mapping relationships between the features of two adjacent frames, and leveraging ReID methods for the association between objects and trajectories. MOT based on natural scenes has also been a trending research topic.