# FETrack: Feature-Enhanced Transformer Network for Visual Object Tracking
## 4 Experiments
### Ablation Study and Analysis  
We conduct a series of ablation studies and extensive analysis to verify the effectiveness of the proposed modules. **Study on the encoder. ** The encoder, as a crucial component of FETrack, is responsible for integrating the dependency between the template and the search region features. To verify the effectiveness of the independent template stream embedded in the encoder, we conduct experiments on the GOT-10k and LaSOT datasets. Specifically, we compare the encoder architectures with different modeling schemes. (1) Two-stream (TS) scheme. Similar to a Siamese network architecture, TS uses two transformer blocks with shared weights to extract the template and the search region features separately and then feeds the search region features into the decoder after a simple fusion module. (2) One-stream (OS) scheme. Similar to the encoder used in OSTrack [10], OS feeds the template and the search region tokens together into the encoder to extract visual features and then feeds the search region features into the decoder. (3) The proposed encoder modeling scheme. In addition to free interaction between the template and the search region in the encoder, we utilize the self-attention mechanism to mitigate interference from the search region. Table 4 shows comparison results of the above modeling schemes. As can be seen from Table 4, our proposed scheme achieves a superior performance on both GOT-10k and LaSOT, validating its effectiveness and advantages. **Study on the template-updating strategy**. We evaluated the proposed online template-updating strategy on the short-term GOT-10k dataset and the long-term LaSOT dataset, respectively. As shown in Table 5, we conduct four sets of experiments with different template-updating strategies. (1) Only use the initial template without template updating. (2) Crop the prediction region of the previous frame and use it as a dynamic template. (3) Set the mean of the confidence scores of the prediction results from historical frames as a threshold to update the template. (4) The proposed online template-updating strategy. As can be observed from Table 5, the tracker with the first strategy performs mediocrely; the tracker with the second strategy performs even worse. This is because the prediction results of the previous frame may be poor, resulting in a low quality of the cropped template as well as the worst performance; the tracker with the third strategy obtains the best performance on the short-term GOT-10k dataset but performs poorly on the long-term \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 (\%)** & \(\mathbf{S}\mathbf{R}_{\mathbf{0.5}}\) (\%) & \(\mathbf{S}\mathbf{R}_{\mathbf{0.75}}\) (\%) & **AUC (\%)** & \(\mathbf{P}_{norm}\) (\%) & \(\mathbf{P}\) (\%)** \\ \hline TS & 71.6 & 80.3 & 68.2 & 65.7 & 74.1 & 71.6 \\ OS (w/o CE) & 74.7 & 84.7 & 71.8 & 69.9 & 79.7 & 76.3 \\ OS (w/CE) & 74.7 & 84.8 & 71.8 & 70.2 & 79.7 & 76.2 \\ \hline ours & **75.1** & **85.3** & **72.0** & **70.6** & **79.9** & **76.5** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Ablation study of the encoder modeling scheme. The best results are in bold fonts. The symbol * indicates the model trained only with the GOT-10k training set. LaSOT dataset. The reason is that while the mean-based threshold appropriately suppresses the low-quality templates and keeps the dynamic template close to the target object of the current frame, it fails to resist long-term target disappearance in the case where the mean is too low. However, compared with previous strategies, the proposed strategy effectively copes with the long-term low-quality templates through the dynamic threshold, prompting our tracker to obtain more reliable results. It can be seen from the above results that our tracker performs well on the long-term sequences and maintains a good performance on the short-term sequences. **Study on the template-filtering approach.