## 5 Experimental Verification
### Quantitative Analysis  
In order to clearly evaluate the performance of each object tracking method, we use the quantitative indicators in the OTB and VOT benchmark datasets to test the performance of each object tracking method. The indicators include the precision plots and success plots in OTB and the expected average coverage (EAO) in VOT. We test the performance of each tracking method for all the video sequences available in the OTB benchmark set. The accuracy and success maps are shown in Figure 12.  
Figure 12 shows that the overall performance of the proposed SiamFCCA on the OTB benchmark set is better compared with the improved SiamFC. Its accuracy and success rate are 6.8% and 7.1% better compared with SiamFC, respectively. In order to further compare the performance of each tracking method in each sub-category of the OTB benchmark set, we estimate the precision data and success plots of each sub-category, as shown in Table 2 and Figure 13. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Tracker** & **All** & **FM** & **BC** & **MB** & **DE** & **IV** & **IPR** & **LR** & **OC** & **OPR** & **OV** & **SV** \\ \hline SiamFCCA & 0.81 & 0.68 & 0.74 & 0.65 & 0.72 & 0.70 & 0.80 & 0.68 & 0.79 & 0.80 & 0.76 & 0.79 \\ SiamFC [2] & 0.76 & 0.68 & 0.72 & 0.60 & 0.67 & 0.69 & 0.72 & 0.56 & 0.72 & 0.74 & 0.69 & 0.76 \\ CCT [2] & 0.78 & 0.69 & 0.73 & 0.67 & 0.81 & 0.70 & 0.71 & 0.43 & 0.72 & 0.78 & 0.62 & 0.73 \\ KCF [24] & 0.74 & 0.58 & 0.75 & 0.62 & 0.75 & 0.72 & 0.72 & 0.38 & 0.73 & 0.72 & 0.61 & 0.67 \\ Struck [25] & 0.66 & 0.60 & 0.59 & 0.54 & 0.53 & 0.57 & 0.61 & 0.55 & 0.56 & 0.59 & 0.48 & 0.62 \\ TLD [26] & 0.61 & 0.54 & 0.43 & 0.50 & 0.52 & 0.54 & 0.58 & 0.35 & 0.56 & 0.59 & 0.53 & 0.60 \\ CXT [27] & 0.58 & 0.52 & 0.44 & 0.52 & 0.42 & 0.51 & 0.62 & 0.37 & 0.50 & 0.58 & 0.52 & 0.55 \\ CSK [28] & 0.54 & 0.37 & 0.59 & 0.32 & 0.48 & 0.49 & 0.55 & 0.41 & 0.50 & 0.54 & 0.36 & 0.49 \\ VTD [29] & 0.58 & 0.35 & 0.57 & 0.37 & 0.51 & 0.58 & 0.61 & 0.17 & 0.55 & 0.63 & 0.45 & 0.59 \\ VTS [30] & 0.58 & 0.35 & 0.58 & 0.37 & 0.50 & 0.59 & 0.58 & 0.19 & 0.54 & 0.61 & 0.44 & 0.57 \\ LSK [31] & 0.50 & 0.36 & 0.50 & 0.29 & 0.50 & 0.45 & 0.53 & 0.30 & 0.52 & 0.52 & 0.45 & 0.47 \\ OAB [32] & 0.51 & 0.42 & 0.45 & 0.36 & 0.50 & 0.40 & 0.48 & 0.38 & 0.49 & 0.51 & 0.41 & 0.53 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The comparison of precision scores of all trackers in all categories. Figure 12: The precision and success plots of the proposed SiamFCCA and other tracking methods on all sequences. By analyzing the success plots and precision data, it is evident that the SiamFCCA performs better compared with SiamFC in terms of most indicators. By analyzing the experiments of each tracking method performed using the OTB benchmark set, we observe that when the SiamFCCA is integrated with the convolution kernel attention module, its overall performance improves significantly compared with the SiamFC and other tracking methods. Especially in the \"In-plane Rotation\", \"Low Resolution\" and \"Out of View\" sub-categories, the precision of SiamFCCA increases by 11.1%, 21.4%, and 10.1% compared with SiamFC, respectively. We also test the proposed SiamFCCA object tracking method using the VOT benchmark set. The evaluation index is mainly reflected by the expected average coverage (EAO). The test results of each tracking benchmark set are shown in Table 3. It is evident from the test results that the tracking performance of the proposed SiamFCCA is better compared with SiamFC as the EAO score increases by 0.04 and its robustness score reduces by 14.98 compared with the original method. Figure 13: The success maps of the proposed SiamFCCA and other tracking methods for each sub-category. The aforementioned experiments show that the SiamFCCA object tracking method proposed in this work is effective. The convolution kernel attention module adjusts the weight of convolution kernel features according to the object characteristics.