# Object-Tracking Algorithm Combining Motion Direction and Time Series
## 4 Experimental Results and Analysis
### Comparison of Results  
Evaluation on LaSOT. We evaluate the performance of our proposed algorithm on the LaSOT test set. AUC (the area under the success plot curve) and P (the accuracy) are used to compare our algorithm against other trackers. Results presented in Table 1 demonstrate that our algorithm outperforms all other compared trackers, with an AUC value of 67.1%, which is higher than that of the STARK-ST50 algorithm (66.4%). Additionally, in terms of accuracy (P), our algorithm achieved a score of 72.2%, which is 1.0 percentage points higher than the STARK-ST50 algorithm's score of 71.2%. Furthermore, the accuracy and success plots are illustrated in Figure 4. It demonstrates the effectiveness of our approach in improving precision and accuracy. Evaluation on TrackingNet. The evaluation of our algorithm on the TrackingNet test set is conducted by submitting it to the official server. The obtained results are presented in Table 2, where \(P_{\text{norm}}\) represents the area under the curve of the normalized accuracy plot. Our algorithm outperforms the STARK-ST50 and SiamRCNN algorithms, achieving a \(P_{\text{norm}}\) value of 87.2%, which is an improvement of 1.1 percentage points and 1.8 percentage points, respectively. These results indicate that our algorithm is ranked as the best performer among all the compared algorithms. It proves the effectiveness of our approach. Figure 4: Accuracy and success rate on the LaSOT test set. Evaluation on GOT-10K. In this experiment, we followed the standard protocol of training solely on the training set and subsequently evaluating the model on the test set, as prescribed by the GOT-10K benchmark. The test results were then submitted to the official server for evaluation. As presented in Table 3, we report the success rate at two distinct thresholds, namely SR0.5 and SR0.75. Our approach surpasses the STARK-ST50 algorithm in both SR0.5 and SR0.75 metrics. Notably, our algorithm achieves an AO value of 69.7%, outperforming the STARK-ST50 algorithm by 1.7 percentage points and the highest performance of all compared algorithms. This indicates that our approach also performs well in enhancing the AO metric. Evaluation on VOT2020. Table 4 presents the evaluation results of our algorithm in terms of robustness and accuracy, as well as the expected average overlap (EAO) metric. Our algorithm shows similar robustness compared to the STARK algorithm, but outperforms it in terms of accuracy. Moreover, our algorithm achieves the best EAO score compared to all other evaluated algorithms. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Tracker** & **Published** & **AUC (\%)** & **P (\%)** \\ \hline LTMU [38] & 2020 & 56.8 & 56.4 \\ DiMP50 [39] & 2019 & 57.9 & 57.7 \\ TrDiMP [22] & 2021 & 63.9 & 66.2 \\ SuperDiMP [40] & 2019 & 63.9 & 66.2 \\ SiamRCNN [19] & 2020 & 64.8 & 68.4 \\ TransT [21] & 2021 & 64.9 & 69.0 \\ AlphaRefine [41] & 2021 & 65.9 & 68.8 \\ STARK-ST50 [25] & 2021 & 66.4 & 71.2 \\ Ours & - & **67.1** & **72.2** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Evaluation on the LaSOT test set. Bold indicates best result. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Tracker** & **Published** & **AUC (\%)** & **P norm (\%)** \\ \hline SiamRPN++ [42] & 2019 & 73.3 & 80.0 \\ DiMP50 [39] & 2019 & 74.0 & 80.1 \\ SiamRDT [43] & 2022 & 74.6 & - \\ SiamFC++ [15] & 2020 & 75.4 & 80.0 \\ PrDiMP50 [44] & 2020 & 75.8 & 81.6 \\ AlphaRefine [41] & 2021 & 80.5 & 85.6 \\ SiamRCNN [19] & 2020 & 81.2 & 85.4 \\ TransT [21] & 2021 & 81.4 & 86.7 \\ STARK-ST50 [25] & 2021 & 81.3 & 86.1 \\ Ours & - & **81.9** & **87.2** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Evaluation on the TrackingNet test set. Bold indicates best result.