## 3 Experiments  
### Experimental Data  
We constructed a dataset comprising 76 video satellite sequences captured using Jilin-1 and GF-03 satellites. All sequences were manually annotated with localization and identity information following the MOT label [52]. The resulting dataset included three major categories: vehicles, ships, and aircraft, with a resolution of 1600 \(\times\) 1200 and a ground sampling distance of \(0.91\sim 0.95\) m. The frame rate of these sequences ranged from 9 to 13 fps. Additionally, we added the VISO dataset [14] as additional training samples. In total, we collected 123 annotated sequences and 6770 objects with unique identities for model training and validation. Among them, 100 sequences were used for training, while 37 sequences (including 7 VISO dataset testing sequences) were used for testing. For the ablation study, we split our training sequences into two halves and used 50% of the  
\begin{table}
\begin{tabular}{l} \hline Pseudo-code of IoU-prior cascade matching \\ \hline
1 **Input**: A video sequence \(V\); tracking model _Net_; IoU matching threshold \(\zeta\) similarity threshold \(\sigma\); new track threshold \(\theta\). \\
2 **Output**: Tracklets \(T\) of the video. \\
3 Initialization: \(T\leftarrow\varnothing\) \\
4 For frame \(f^{(t)}\) in V **do:** \\
5 \(D^{(t)}\), \(M^{(t)}\gets Net(f^{(t)})\) \\
6 \(f^{*}\) First association */ \\
7 \(f^{*}\) Associate \(D^{(t)}\) and \(T^{(t-1)}\) using IoU threshold \(\zeta=0.60\) */ \\
8 For \(d\), \(t\in D^{(t)}\),\(T^{(t-1)}\) do: \\
9 If IoU(\(t|d\)) \(\geq\) \(\zeta\) then \\
10 adding detection \(d\) to track \(t\) \\
11 end if \\
12 end for \\
13 \(D^{(t)}_{um\_i}\leftarrow\) remaining object boxes from \(D^{(t)}\) \\
14 \(T^{(t-1)}_{um\_i}\leftarrow\) remaining tracks from \(T^{(t-1)}\) \\
15 \(f^{*}\) Second association */ \\
16 \(f^{*}\) Associate \(D^{(t)}_{um\_i}\) and \(T^{(t-1)}_{um\_i}\) using the Greedy Order Algorithm */ \\
17 \(Cost^{(t)}\leftarrow\) where \(l2\ distance(D^{(t)}_{um\_i}+M^{(t)}_{um\_i}|T^{(t-1)}_{um\_i})\leq\sqrt{ width^{2}_{\tau^{(t-1)}_{um\_i}}+height^{2}_{\tau^{(t-1)}_{um\_i}}}\) \\
18 Linear assignment using the greedy algorithm with \(Cost^{(t)}\) \\
19 \(D^{(t)}_{um\_i}\leftarrow\) remaining object boxes from \(D^{(t)}_{um\_i}\) \\
20 \(T^{(t-1)}_{um\_i}\leftarrow\) remaining tracks from \(T^{(t-1)}_{um\_i}\) \\
21 /* Third association */ \\
22 /* Associate \(D^{(t)}_{um\_i}\) & \(T^{(t-1)}_{um\_i}\) using ReID similarity threshold \(\sigma=0.3\) */ \\
23 For \(d_{um\_i}\), \(t_{um\_i}\in D^{(t)}_{um\_i}\), \(\tau^{(t-1)}_{um\_i}\)do: \\
24 If (cosine similarity\(\left(emb[d_{um\_ii}]\left|emb[t_{um\_ii}]\right|)\geq\sigma\) and \(t_{um\_ii}\) still alive) then \\
25 adding detection \(d_{um\_ii}\) to track \(t_{um\_ii}\) \\
26 end \\
27 Else if \(\text{ conf}\big{(}d_{um\_ii}\big{)}\geq\theta\) where \(\theta=0.4\)then \\
28 Initial new track based on \(d_{um\_ii}\) \\
29 end \\
30 end \\ \hline \end{tabular}
\end{table}
Table 1: Pseudo-code of IoU-prior cascade matching in the MFACNet, which effectively utilizes displacement prediction, localization, and appearance information to manage object trajectories.  
sequences for training and the remaining 50% for performance validation. Part of the test samples of our dataset is shown in Figure 7.