# Object-Tracking Algorithm Combining Motion Direction and Time Series
## 4 Experimental Results and Analysis
### Ablation Study  
In the ablation experiments, we optimized the STARK algorithm using DIoU, CIoU [50], and the SIoU loss function, which included directional guidance, as well as the TRSM module. The resulting algorithm was then validated on the TrackingNet and LaSOT test set to evaluate the impact of each loss function and the TRSM module on algorithm performance. The experimental results are shown in Table 6.  
The experimental results demonstrate that the impact of DIoU and CIoU on algorithm performance is minimal, resulting in an improvement of only 0.3 percentage points in the \(P_{\text{norm}}\) metrics and slight improvements in the other metrics. However, using the SIoU loss function with angle information improved the algorithm's performance on the \(P_{\text{norm}}\) metrics in the TrackingNet dataset by 0.8, the P metrics in LaSOT by 0.6, and the AUC metrics in both datasets. These results suggest that angle information is beneficial for improving the algorithm's precision and success rate.  
Furthermore, when the TRSM module is used in isolation, the \(P_{\text{norm}}\) metrics increase by 0.5 percentage points, and the other metrics also improve. This underscores the effectiveness of the TRSM module in optimizing the template update of the STARK algorithm. These findings indicate that mining the time series information of tracking results can enhance  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**STARK-ST50**} & \multirow{2}{*}{**DIoU**} & \multirow{2}{*}{**CIoU**} & \multirow{2}{*}{**SIoU**} & \multirow{2}{*}{**TRSM**} & \multicolumn{2}{c}{**TrackingNet**} & \multicolumn{2}{c}{**LaSOT**} \\ \cline{5-8}  & & & & & & \(P_{\text{norm}}\) **(\%)** & **AUC (\%)** & **P (\%)** & **AUC (\%)** \\ \hline
1 & \(\surd\) & & & & & 86.1 & 81.3 & 71.2 & 66.4 \\
2 & \(\surd\) & \(\surd\) & & & & 86.4 & 81.4 & 71.3 & 66.4 \\
3 & \(\surd\) & & \(\surd\) & & & 86.4 & 81.4 & 71.4 & 66.5 \\
4 & \(\surd\) & & & \(\surd\) & & 86.9 & 81.6 & 71.8 & 66.8 \\
5 & \(\surd\) & & & & \(\surd\) & 86.6 & 81.5 & 71.6 & 66.7 \\
6 & \(\surd\) & & & \(\surd\) & \(\surd\) & 87.2 & 81.9 & 72.2 & 67.1 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Ablation study on TrackingNet and LaSOT test set; “\(\surd\)” indicates that the method was used.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Tracker** & **Published** & **EAO** & **Accuracy** & **Robustness** \\ \hline KCF [48] & 2014 & 0.154 & 0.407 & 0.432 \\ SiamFC [14] & 2016 & 0.179 & 0.418 & 0.502 \\ ATOM [45] & 2019 & 0.271 & 0.462 & 0.734 \\ DIMP50 [39] & 2019 & 0.274 & 0.457 & 0.74 \\ UPDT [49] & 2018 & 0.278 & 0.465 & 0.755 \\ SuperDiMP [40] & 2019 & 0.305 & 0.477 & 0.786 \\ STARK-ST50 & & & & \\
[25] & 2021 & 0.308 & 0.478 & **0.799** \\ Ours & - & **0.313** & **0.486** & 0.798 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Evaluation on the VOT2020 dataset. Bold indicates best result.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Trackers** & **Speed (fps)** & **Params (M)** & **FLOPs (G)** \\ \hline Our & 25 & 29.8 & 12.9 \\ STARK-ST50 & 27 & 28.2 & 12.8 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of the speed, FLOPs, and Params.  
the algorithm's ability to assess the reliability of tracking results. When used in conjunction with the SIoU loss function, the TRSM module and the SIoU loss function complement each other, resulting in further improvements in tracking precision and success rate.