# AR Long-Term Tracking Combining Multi-Attention and Template Updating
## 4 Experimental Analysis
### Analysis of Results
#### 4.2.1 Quantitative Analysis
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Trackers** & \
\begin{tabular}{c} **Illumination** \\ **Variation** \\ \end{tabular} & **Occlusion** & **Deformation** & **Motion Blur** & **Fast Motion** & **Out-of-View** &
\begin{tabular}{c} **Background** \\ **Clutters** \\ \end{tabular} \\ \hline KCF & 0.728 & 0.749 & 0.740 & 0.650 & 0.602 & 0.650 & 0.753 \\ SiamFC & 0.709 & 0.802 & 0.744 & 0.700 & 0.721 & 0.777 & 0.732 \\ SiamRPN & 0.817 & 0.790 & 0.810 & 0.777 & 0.831 & 0.824 & 0.813 \\ CFNet & 0.743 & 0.768 & 0.669 & 0.676 & 0.718 & 0.721 & 0.785 \\ Staple [34] & 0.727 & 0.775 & 0.788 & 0.670 & 0.642 & 0.669 & 0.730 \\ DaSiamRPN & 0.848 & 0.838 & 0.900 & 0.786 & 0.791 & 0.757 & 0.868 \\ SiamDW & 0.841 & 0.855 & 0.906 & 0.716 & 0.732 & 0.734 & 0.847 \\ ours & 0.888 & 0.884 & 0.860 & 0.946 & 0.959 & 0.805 & 0.869 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Precision under different attributes. The best three results are marked in red, green, and blue bold fonts, respectively. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Trackers** & \
\begin{tabular}{c} **Illumination** \\ **Variation** \\ \end{tabular} & **Occlusion** & **Deformation** & **Motion Blur** & **Fast Motion** & **Out-of-View** &
\begin{tabular}{c} **Background** \\ **Clutters** \\ \end{tabular} \\ \hline KCF & 0.581 & 0.618 & 0.671 & 0.595 & 0.557 & 0.650 & 0.672 \\ SiamFC & 0.679 & 0.769 & 0.705 & 0.666 & 0.699 & 0.754 & 0.705 \\ SaimRPN & 0.757 & 0.736 & 0.719 & 0.715 & 0.784 & 0.824 & 0.777 \\ CFNet & 0.688 & 0.727 & 0.628 & 0.618 & 0.680 & 0.706 & 0.725 \\ Staple & 0.692 & 0.732 & 0.774 & 0.628 & 0.605 & 0.586 & 0.698 \\ DaSiamRPN & 0.822 & 0.817 & 0.877 & 0.731 & 0.758 & 0.761 & 0.847 \\ SiamDW & 0.772 & 0.808 & 0.846 & 0.699 & 0.705 & 0.726 & 0.775 \\ ours & 0.771 & 0.845 & 0.852 & 0.804 & 0.844 & 0.776 & 0.848 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Success under different attributes. The best three results are marked in red, green, and blue bold fonts, respectively. Figure 7: Performance of different algorithms on OTB100 dataset. target), and ARC (the ratio of the bounding box aspect ratio is outside the range). Table 5 indicates that our tracker performs better than others in most cases (including STARK [35]). Results evaluated on the VOT-LT2020 and OxUvA:  
VOT-LT2020. The VOT-LT2020 dataset is a long-term tracking dataset consisting of 50 video sequences with a total of more than 23,000 frames of images and covers various challenges in target tracking, such as target occlusion, target disappearing for long periods of time, background interference, etc. It uses Precision (Pr) and Recall (Re) to measure the performance of the tracker, and the calculation process of F-score is \(F=\frac{2 Pr_{Re}}{Pr+Re}\). We compare the performance of the trackers on the VOT-LT2020 and report the results in Table 6. Our tracker achieves the top three performances. Our tracker obtains a recall rate of 0.713, which is 1.8% higher than STARK-ST50. \begin{table}
\begin{tabular}{c c c c} \hline
**Trackers** & **Success** & **Norm Precision** & **Precision** \\ \hline KCF & 0.178 & 0.190 & 0.166 \\ Staple & 0.243 & 0.27 & 0.239 \\ SiamFC & 0.336 & 0.420 & 0.339 \\ SiamDW & 0.347 & 0.437 & 0.329 \\ SiamPPN++ & 0.495 & 0.570 & 0.493 \\ ATOM & 0.499 & 0.570 & 0.497 \\ DaSiamRPN & 0.515 & 0.605 & 0.529 \\ STARK & 0.671 & 0.770 & 0.713 \\ ours & 0.651 & 0.740 & 0.692 \\ \hline \end{tabular}
\end{table} 
Table 4: Comparison on LaSOT. The best three results are marked in red, green, and blue bold fonts, respectively. Figure 8: Performance of different algorithms on the LaSOT dataset.