###### Abstract  
Efficient multi-object tracking (MOT) in satellite videos is crucial for numerous applications, ranging from surveillance to environmental monitoring. Existing methods often struggle with effectively exploring the correlation and contextual cues inherent in the consecutive features of video sequences, resulting in redundant feature inference and unreliable motion estimation for tracking. To address these challenges, we propose the MFACNet, a novel multi-frame features aggregating and inter-feature correlation framework for enhancing MOT in satellite videos with the idea of utilizing the features of consecutive frames. The MFACNet integrates multi-frame feature aggregation techniques with inter-feature correlation mechanisms to improve tracking accuracy and robustness. Specifically, our framework leverages temporal information across the features of consecutive frames to capture contextual cues and refine object representations over time. Moreover, we introduce a mechanism to explicitly model the correlations between adjacent features in video sequences, facilitating a more accurate motion estimation and trajectory associations. We evaluated the MFACNet using benchmark datasets for satellite-based video MOT tasks and demonstrated its superiority in terms of tracking accuracy and robustness over state-of-the-art performance by 2.0% in MOTA and 1.6% in IDFI. Our experimental results highlight the potential of precisely utilizing deep features from video sequences. multi-object tracking; satellite video; inter-feature mapping; feature aggregation +
Footnote †: journal:  
0020  
* Video sequences contain numerous objects with dense spatial distributions and complex dynamics. Furthermore, due to the limited resolution of video sequences, individual objects are represented by dozens of pixels and lack fine texture details, which results in significant challenges in object detection and low discriminability between different objects. A sample of objects in video satellite sequences, along with their pixel statistics, is shown in Figure 2;
* When video satellites capture videos with a tiny off-nadir angle, the resulting video sequences can be approximated as being captured in a near-vertical manner. Compared with nature scenarios, the object sizes are relatively consistent, and fewer occlusions occur between objects. The motion patterns of objects are similar, and their trajectories tend to be linear. Additionally, unlike conventional remote-sensing imagery, remote-sensing video satellites capture imagery with high frame rates, which results in high scene overlap in the captured landscapes and significant overlap of the same object between adjacent frames. Figure 1: Instances of different objects in high-resolution remote-sensing imagery and video satellite sequence, along with their pixel statistics. (**a–c**) Instances of vehicles, vessels, and aircraft in video satellite sequence. (**d–f**) Instances of vehicles, vessels, and aircraft in high-resolution remote-sensing imagery from the DOTA dataset. Figure 2: Pixel statistics for vehicles, vessels, and aircraft in video satellite sequences with a ground sample distance (GSD) of 1.1 m.  
Recently, a trend of rapid iteration exists in algorithms for MOT in video satellites. Early research primarily relied on background modeling-based object detection algorithms [1; 2; 3; 4; 5; 6; 7], such as background subtraction-based [8], optical flow-based [9], and frame difference-based [10] methods. These trackers initially extract the regions of interest that may contain objects, then exclude the background noise and reduce false alarms using prior knowledge, noise models, and motion models. etc. Similarly, background modeling algorithms in machine learning [11; 12] have also been applied in the research of object tracking in video satellite imagery, such as the Gaussian mixture model (GMM) and ViBE [13]. For instance, [PERSON], et al. [12] used a visual background extractor (ViBE) to perform foreground separation on roughly segmented motion-salient regions.