# FETrack: Feature-Enhanced Transformer Network for Visual Object Tracking
## 4 Experiments
### Comparison with State of the Arts
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Source**} & \multicolumn{4}{c}{**GOT-10k \({}^{*}\)**} & \multicolumn{4}{c}{**LaSOT**} & \multicolumn{4}{c}{**TrackingNet**} & \multicolumn{4}{c}{**LaSOT\({}_{ext}\)**} \\ \cline{3-14}  & & **AO (\%)** & \(SR_{0.8}\) (\%) & \(SR_{0.75}\) (\%) & **AUC (\%)** & \(P_{norm}\) (\%) & **P (\%)** & **AUC (\%)** & **P\%** & **AUC (\%)** & **AUC (\%)** & **P\%** \\ \hline STARK [42] & ICCV21 & 68.8 & 78.1 & 64.1 & 67.1 & 77.0 & 72.2 & 82.0 & 86.9 & 79.1 & - & - & - \\ TransT [26] & CVPR21 & 67.1 & 76.8 & 60.9 & 64.9 & 73.8 & 69.0 & 81.4 & 86.7 & 80.3 & 45.1 & 51.3 & 51.2 \\ OSTrack-256 [10] & ECCV22 & 71.0 & 80.4 & 68.2 & 69.1 & 78.7 & 75.2 & 83.1 & 87.8 & 82.0 & 47.4 & 57.3 & 53.3 \\ MicroMerm-1k [27] & CVPR22 & 71.2 & 79.9 & 65.8 & 67.9 & 77.3 & 73.9 & 82.6 & 87.7 & 81.2 & - & - & - \\ SwinTrack [43] & NNS22 & 72.4 & 80.5 & 67.8 & 71.3 & 79.4 & 76.5 & 84.0 & 88.6 & 82.8 & 49.1 & 59.2 & 55.6 \\ TATrack [11] & AAA23 & 73.0 & 83.3 & 68.5 & 69.4 & 78.2 & 74.1 & 83.5 & 88.3 & 81.8 & - & - & - \\ GRM [14] & CVPR23 & 73.4 & 82.9 & 70.4 & 69.9 & 79.3 & 75.8 & 84.0 & 88.7 & 83.3 & - & - & - \\ VideoTrack [12] & CVPR23 & 72.9 & 81.9 & 69.8 & 70.2 & 79.2 & 76.4 & 83.8 & 88.7 & 83.1 & - & - & - \\ SepTrack-256 [53] & CVPR23 & 74.7 & 84.7 & 71.8 & 69.9 & 79.7 & 76.3 & 83.3 & 88.3 & 82.2 & 49.5 & 60.8 & 56.3 \\ ARTrack-256 [36] & CVPR23 & 73.5 & 82.2 & 70.9 & 70.4 & 79.5 & 76.6 & 84.2 & 88.7 & 83.5 & 46.4 & 56.5 & 52.3 \\ DyTrack [41] & Preprint24 & 71.4 & 80.2 & 68.5 & 69.2 & 78.9 & 75.2 & 82.9 & 87.3 & 81.2 & 48.1 & 58.1 & 54.6 \\ ODTrack [32] & Preprint24 & 74.6 & 85.6 & 71.9 & _69.6_ & 79.5 & 75.4 & 84.1 & 89.0 & 82.8 & - & - & - \\ \hline FETrack-256 & ours & 75.1 & 85.3 & 72.0 & 70.6 & 79.9 & 76.5 & 83.6 & 88.5 & 82.4 & 49.8 & 61.3 & 56.7 \\ FETrack-384 & ours & 74.9 & 84.8 & 71.7 & 71.8 & 81.2 & 77.9 & 84.1 & 89.3 & 83.8 & 50.7 & 61.9 & 57.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison with state-of-the-art methods on four large-scale benchmarks, including GOT-10k, LaSOT, TrackingNet, and LaSOT\({}_{ext}\). The symbol * indicates the model trained only with the GOT-10k training set. The best and the second-best results are marked in red and blue, respectively. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & **STARK [42]** & **TransT [26]** & **TrDiMP [45]** & **OSTrack-256 [10]** & **Mixformer [27]** & **AiATrack [28]** & **SeqTrack [35]** & **FETrack-256 (Ours)** & **FETrack-384 (Ours)** \\ \hline OTB100 & 68.5 & 69.4 & 70.8 & 68.1 & 70.4 & 69.6 & 68.3 & 71.5 & 70.9 \\ NFS30 & 65.2 & 65.7 & 65.8 & 66.5 & 66.4 & 67.9 & 67.6 & 68.2 & 69.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison results of FETrack on two small-scale benchmarks, including NFS30 and OTB100. The tracking results are evaluated using the AUC (%) score. The best and the second-best results are marked in red and blue, respectively. **TrackingNet. ** TrackingNet [19] is a large short-term tracking dataset containing \(511\) test video sequences and covering various object categories and scenarios. We submit tracking results to the official evaluation server. From Table 2, we can observe that our FETrack-384 obtains 84.1% and 89.3% in the AUC and \(P_{norm}\), respectively, exhibiting a competitive performance compared to the comparison trackers. This suggests that our FETrack also has advantages in short-term tracking scenarios. **OTB100 and NFS30. ** We also evaluate the proposed FETrack on two small-scale benchmarks (i.e., NFS30 [22] and OTB100 [21]). As can be seen from Table 3, our FETrack-256 and FETrack-384 perform excellently on both benchmarks and achieve the state-of-the-art performance. These results further validate the flexibility and effectiveness of FETrack across different scales.