## 2 Materials and Methods
### Solution for Object Occlusion  
The correlation filter algorithms track the object quickly and accurately under normal circumstances, but when the object obstacles block the object, the object tracking fails if the area of the tracked object is reduced or even disappears temporarily. The problem of object occlusion has constantly challenged object tracking, and therefore studying and solving this problem has theoretical and practical significance. Objects in the satellite videos are very small, occupying only a few pixels, so their complete occlusion is expected. As illustrated in Figure 6, when the car passes under the bridge, it becomes occluded and eventually disappears, causing most trackers to lose the object and struggle to re-track it when it re-appears. Therefore, object occlusion has always been challenging for computer vision applications.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **Plane1** & **Plane2** & **Car1** & **Car2** & **Car3** & **Car4** & **Car5** & **Car6** & **Average** \\ \hline KF & 35 & 32 & 33 & 29 & 30 & 31 & 28 & 34 & **32** \\ AKF & 15 & 14 & 16 & 13 & 15 & 14 & 13 & 17 & **15** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Convergence frames of two filters in eight video sequences. The bold values denote the average frame numbers on all video sequences.  
Hence, the following sub-problems need to be solved correctly to track the occluded object.  
(1) _Occlusion Detection_: The algorithm must detect that the object is occluded (Figure 6a).  
(2) _Occlusion Processing_: When the object is completely or partially occluded (Figure 6b), the algorithm must track the object to ensure that the occluded object is not lost.  
(3) _End of Occlusion Detection_: The algorithm must be able to detect the end of the occluded object and correctly re-track the object when it re-appears (Figure 6c).  
To solve the above problems, our algorithm involves the following steps:  
(1) Update the tracking model only when the confidence of the object tracking is relatively high to avoid the object model from being degraded and simultaneously increase the processing speed. We use the SDM value of the correlation response patch to judge the tracking quality. The smaller the SDM value, the better the tracking result. Object tracking has several significant difficulties: object deformation, illumination variation, the object's blur motion, the fast motion of the object, background clutter, object rotation, scale variation, and object occlusion. These reduce the SDM value of the relevant response patch. However, illumination variation, scale variation, and motion blur are not evident in satellite videos and thus can be neglected in object tracking. The primary reason for a higher SDM value in the correlation response patch is the object's partial or complete occlusion. In summary, the SDM value can be used to judge whether the object is occluded or not, and choosing the appropriate threshold is the key to solving the object occlusion problem.  
(2) When the object is occluded, we avoid erroneous features from interfering with the object features by stopping updating the tracker's filter. Moreover, the position calculated by the KCF tracker is not accurate and cannot be used as the object position of the current frame. Therefore, it is necessary to utilize the KF algorithm to predict the object's position.  
(3) We compare the SDM value with the occlusion threshold. If the SDM value is less than the occlusion threshold, the object is not occluded and can be tracked normally. Otherwise, the object is partially or entirely occluded, and KF is exploited to track the occluded object.  
Using KF to solve the object occlusion problem when the object re-appears allows to relocate it precisely. The corresponding pseudocode of the proposed improved method is presented in Algorithm 1.