# FETrack: Feature-Enhanced Transformer Network for Visual Object Tracking
## 4 Experiments
### Visualization and Qualitative Analysis  
To further validate the effectiveness and superiority of the proposed FETrack, we subjectively compare the performance of various trackers. First, to verify the contribution of the proposed encoder to our FETrack, we visualize the attention maps of different layers of the encoder in Figure 5. From Figure 5, we can observe that compared with the encoder adopting the free bidirectional interaction stream paradigm, our encoder focuses on the target object in the search region and suppresses the interference in the background by retaining the pure template features, which is beneficial to locate a more accurate target position. Secondly, to investigate how the sequence-learning-based causal transformer acquires the target state, Figure 6 visualizes the cross-attention maps of the last decoder block. From Figure 6, it can be seen that the attention gradually changes from the dispersed state to the focused state and ultimately focuses on the key position of the target as the bounding box tokens are generated sequentially. Notably, our FETrack pays attention to the edge of the target adaptively when predicting the coordinates in various cases. This also indicates that our tracker possesses the ability to accurately locate the target. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**GOT-10k ***} & \multicolumn{3}{c}{**LaSOT**} \\ \cline{2-6}  & **AO (\%)** & \(SR_{0.5}\) (\%) & **AUC (\%)** & \(P_{norm}\) (\%)** & **MACs (G)** \\ \hline w/o TF & 75.0 & 85.1 & 70.3 & 79.0 & 34.1 \\ w/TF & **75.1** & **85.3** & **70.6** & **79.9** & **27.6** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Ablation study of the template-filtering (TF) approach. The best results are in bold fonts. The symbol * indicates the model trained only with the GOT-10k training set. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**GOT-10k ***} & \multicolumn{3}{c}{**LaSOT**} \\ \cline{2-7}  & **AO (\%)** & \(SR_{0.5}\) (\%) & \(SR_{0.75}\) (\%) & **AUC (\%)** & \(P_{norm}\) (\%) & **P (\%)** \\ \hline w/o Update & 72.5 & 82.3 & 68.3 & 69.2 & 77.0 & 74.8 \\ w/Previous & 69.8 & 78.7 & 62.8 & 63.5 & 70.1 & 68.5 \\ Mean & **75.4** & **85.5** & **72.6** & 68.6 & 77.7 & 73.6 \\ \hline ours & 75.1 & 85.3 & 72.0 & **70.6** & **79.9** & **76.5** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Ablation study of the template-updating strategy. The best results are in bold fonts. The symbol * indicates the model trained only with the GOT-10k training set. To visualize the tracking performance, Figure 7 compares the tracking results of the proposed FETrack with three state-of-the-art trackers on four sequences of the LaSOT dataset. These sequences mainly have challenges such as similar targets, occlusion,  
Figure 5: Comparison of attention maps of different layers of the encoder. The two rows of attention maps corresponding to each search region are the attention from the search region to the template in different blocks of the encoder of our FETrack and OSTrack, respectively. Figure 6: Cross-attention map of the decoder. (**a**) Search region. (**b**–**e**) Cross-attention map of the target token to the search region in the last layer of the decoder. and scale variation, and these challenges may occur individually or simultaneously. As can be seen from Figure 7, other trackers may lose targets during tracking, while our FETrack achieves superior tracking results. Specifically, the proposed FETrack accurately discriminates similar targets in the bird-2 sequence, overcomes the occlusion challenge in the crab-3 sequence, and successfully adapts to the scale variation in the kite-10 sequence. Especially, our FETrack simultaneously overcomes the challenges of occlusion and similar targets and achieves stable tracking of the target in the glraffa-2 sequence. These results also indicate that the proposed FETrack can effectively deal with the challenges of similar targets, occlusion, and scale variation.