## 4 Results and Analysis
### Tracking Result Analysis  
#### 4.3.1 Quantitative Evaluation  
Figures 12 and 13 illustrate our algorithm's precision and success plots against other classical methods on eight satellite videos. The proposed method achieves the optimal tracking results in the most challenging scenarios. Compared with KCF, the suggested tracker improves tracking accuracy by utilizing CNN features and trajectory compensation correction. Given that the original KCF tracking accuracy is low and the results are not  
Figure 11: Visualization of some tracking results for the occluded object. The data in the parenthesis marked by upper case letters denote the current frame’s SDM value of the images with the corresponding lower case letters. (**a**) Occlusion process of Car3 sequences. (**b**) Occlusion process of Car4 sequences. (**c**) Occlusion process of Car5 sequences. (**d**) Occlusion process of Car6 sequences. Figure 10: Success plot for per threshold and the legend is the AUC per threshold. appealing, modifying KCF to handle various situations can significantly improve the object tracking performance. To analyze our algorithm's performance, we divide the satellite videos into two parts: four video sequences without object occlusion and four video sequences with object occlusion. The tracking results of the unoccluded object on the Plane1, Plane2, Car1, and  
Figure 12: Precision plots of eight video sequences involving object sequences without and with occlusion. The legend in the precision plot is the corresponding precision score per object tracker. Figure 13: Success plots of eight video sequences involving object sequences without and with occlusion. The legend in the success plot presents the corresponding AUC per object tracker. Car2 sequences are presented in Figure 12 and Table 4. Regarding the Plane1 and Car1 sequences, our algorithm ranks first and attains better results on the AUC, success score, and precision score metrics. Compared to the SiamRPN tracker, the proposed method improves the AUC, success score, and precision score by approximately 7%, 6%, and 2.3% for Plane1 and 3.3%, 4.1%, and 2.7% for the Car1 object, respectively. The SiamRPN tracker combines the region proposal network (RPN) with a Siamese network and directly classifies and regresses the position of \(K\) anchor points on different scales and aspect rations at the same location, improving the object tracking accuracy. However, on the Car2 and Plane2 sequences, the SiamRPN algorithm ranks first. Nevertheless, our algorithm's AUC, success score, and precision score are increased by approximately 2%, 1%, and 5%, respectively, for the Plane2 object and 4.2%, 3%, and 1% for the Car2. Thus, the developed algorithm does not differ substantially from the SiamRPN tracker in terms of performance and robustness. Overall, the SiamRPN algorithm performs poorer than our method because it is trained offline, the model parameters are not updated online, and the number of anchor points at the same position should be designed to be large enough. The latter undoubtedly increases the number of network parameters and generates a large number of negative samples, resulting in unbalanced positive and negative samples, slightly lowering the tracking accuracy of SiamRPN compared to the suggested algorithm.