# Transformer-Based Visual Object Tracking with Global Feature Enhancement
## 4 The Experiments
### Quantitative Experiments On Public Datasets  
Four widely used tracking benchmarks, VOT2018 [37], LaSOT [33], UAV123 [38], and OTB100 [39], are used to evaluate the performance of the proposed GFETrack. **VOT2018:**  
This dataset consists of 60 challenging video sequences. The accuracy (A), robustness (R), and expected average overlap (EAO) are used as the evaluation criteria for VOT2018. An excellent tracker typically has high A and EAO scores, but low R scores. \begin{table}
\begin{tabular}{c c c} \hline
**Trackers** & **Speed (fps)** & **Params (M)** \\ \hline GFETrack & 55 & 20.1 \\ TransT & 54 & 23.0 \\ STARK-ST50 & 42.2 & 23.3 \\ SiamRPN++ & 35.0 & 54.0 \\ \hline \end{tabular}
\end{table} 
Table 2: The comparison experiments of tracking speed and the number of parameters. Table 3 reveals that GFETrack excels in accuracy with a score of 0.637, surpassing the baseline tracker (TransT). However, it is noteworthy that our proposed tracker demonstrates relatively average performance in terms of EAO and R scores. We attribute this observation primarily to a deliberate choice made during the feature extraction stage, wherein we removed the last layer of ResNet50. While this enhances accuracy by focusing on specific features, it inadvertently results in the loss of some local information from input images. This loss of local information likely contributes to the observed dip in both robustness (R) and expected average overlap (EAO) scores. The intentional removal of the last ResNet50 layer was aimed at optimizing the tracker for accuracy-focused scenarios. However, we acknowledge that this design choice comes at the cost of reduced robustness, as evidenced by the comparatively lower R and EAO scores. Future work may explore strategies to balance this trade-off, perhaps through the integration of additional contextual information or the use of alternative feature extraction techniques. **LaSOT:**  
LaSOT is a large-scale, long-term, visual object tracking dataset that includes over 10000 video sequences covering 142 different object categories. In Figure 5, the success and precision plots are presented. The proposed GFETrack outperformed other trackers, achieving the highest success rate of 67.5%, which is 2.6% higher than the baseline tracker, TransT. It also reached the highest precision rate of 73.2%, surpassing TransT by 4.2%. To further analyze the proposed GFETrack, Figure 6 also presents the radar chart for various tracking challenges on the LaSOT dataset. As shown in Figure 6, compared to some state-of-the-art trackers, such as DiMP [42], DaSiaRPN [44], ATOM [41], TransT [9], SiamRPN++ [36], TrSiam [45], and PrDiMP [46], the proposed trackers demonstrate better performance in most of the tracking challenges, especially in viewpoint change, low resolution, and camera motion. Figure 5: The success and precision plots of the proposed algorithm and some other state-of-the-art trackers on LaSOT dataset. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **ECO [40]** & **ATOM [41]** & **SiamRPN++** & **DiMP [42]** & **Ocean-** & **TransT [9]** & **GFETrack** \\ \hline EAO \(\uparrow\) & 0.280 & 0.401 & 0.414 & 0.440 & 0.489 & 0.266 & 0.362 \\ A \(\uparrow\) & 0.484 & 0.590 & 0.600 & 0.597 & 0.592 & 0.587 & 0.637 \\ R \(\downarrow\) & 0.276 & 0.204 & 0.234 & 0.153 & 0.117 & 0.384 & 0.281 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The comparison experiments of different trackers on VOT2018 dataset. The red, green, and blue fonts indicate the top 3 trackers. The detailed precision plots of different tracking challenges are shown in Figure 7. The proposed tracker performs well in various scenarios, such as viewpoint change, camera clutter, and low resolution. This indicates that the proposed method has significant potential in handling challenging scenarios. We believe the primary reason is that the proposed tracker focuses more on the object itself, leading to increased precision and robustness. Figure 6: The AUC scores of different tracking algorithms under 13 tracking challenges.