###### Abstract
Despite the differences in application domains, some MOT algorithms based on natural scenes show remarkable potential in remote-sensing video sequences as well. Different from video satellites, MOT research based on natural scenes mainly focuses on common application scenes, such as public security, city traffic, autopilot, and sports broadcasting [35]. Several approaches have been proposed to address these issues. ByteTrack [34] introduces a two-stage cascaded matching logic that utilizes low-confidence detections to explore potential trajectory matches. OC-SORT [36] incorporates motion inertia into Kalman filter observations. GSTD [37] employs a graph model to match detections and recover low-confidence objects. FairMOT [38] employs an anchor-free detector with parallel ReID feature encoding heads to predict object detections and pixel-level ReID features simultaneously. Center-Track [39] integrates information from two consecutive frames and directly predicts the relative displacements of object centers. TraDeS [40] supervises ReID feature encoding through cost volume and extracts similarity information to infer the object displacement between frames. To efficiently incorporate motion cues from video sequences and fully utilize appearance information and motion prediction for robust tracking, we propose an online joint detection and tracking (JDT) framework called the MFACNet (multi-frame feature aggregating and correlation network). The MFACNet leverages the feature aggregation wrapper (FAW) module to aggregate consecutive feature information and enhance the current feature representation in channel dimensions. The feature-mapping motion estimation (FMME) module computes a correlation volume between the features of two adjacent frames, thereby constructing an inter-frame correlation map based on deep features, which enables the rapid estimation of object displacements. The IoU-prior cascaded matching method sequentially associates object detections with trajectories based on both spatial localization and appearance information. Furthermore, we developed an end-to-end tracking frame that adheres to the JDT paradigm as well as online inference. Figure 3: General architecture of online DL methods for MOT in video satellites. (**a**) Architecture of the TBD methods. (**b**) Architecture of the JDT methods; the dashed line indicates that the portion can be omitted; the red boxes represent the visualized object tracking results. The experimental results demonstrated significant improvements in key MOT metrics on video satellite tracking datasets using the MFACNet. The performance of our framework is highly competitive and achieves optimality. The contributions can be summarized as follows. 1. We proposed an end-to-end JDT framework called the MFACNet, which extracts the static features of man-made objects in video sequences while effectively integrating dynamic cues from consecutive frames. This framework significantly enhances the detection and tracking performance of the network;
2. For detection, we devised a lightweight feature aggregation wrapper (FAW) module, which utilizes sets of deformable convolutions (DCN) [41] to extract correlated information in different channel dimensions from the feature groups of multiple frames. All of the information is then employed to enhance the feature representation of the current frame. For tracking, we employed an end-to-end learnable feature-mapping motion estimation (FMME) module to estimate the displacement of individual objects. Regarding object-trajectory matching, we designed an IoU-prioritized cascaded matching scheme that effectively utilizes both localization and appearance information to generate and manage object trajectories;
3. We conducted the training and validation of our model on a video satellite object tracking dataset, which was constructed from the video sequences captured by the Jilin-1 video satellite and GF-3 high-resolution video satellite.