# FETrack: Feature-Enhanced Transformer Network for Visual Object Tracking
## 4 Experiments
### Comparison with State of the Arts  
We compare our FETrack with state-of-the-art trackers on six benchmark datasets, including four large-scale datasets and two small-scale ones. Table 2 shows comparison results on the large-scale GOT-10k, LaSOT, LaSOT\({}_{ext}\), and TranckingNet benchmarks, and Table 3 presents comparison results on the small-scale NFS30 and OTB100 benchmarks. For the comparison methods, we obtained the source codes and pre-trained models released by the authors of the paper and implemented them on our GPU to produce experimental results while maintaining the original experimental settings. **Evaluation protocol:** We follow other VOT methods and use the average overlap percentage (AO), the success rate at the 0.5 threshold (\(SR_{0.5}\)), and the success rate at the 0.75 threshold (\(SR_{0.75}\)) to measure the tracking performance on the GOT-10k dataset. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Data augmentation & Horizontal flipping and brightness jitter \\ Batch size & 32 \\ Optimizer & Adam \\ Encoder’s learning rate & \(10^{-5}\) \\ Decoder’s learning rate & \(10^{-4}\) \\ Weight decay & \(10^{-4}\) \\ Training epochs & 500 \\ Sample images per epoch & 60,000 \\ Learning rate decay epoch & 400 \\ Decay rate & 0.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Detailed training settings. For other benchmark tests, we use the percentage of the area under the success rate curve (AUC) score, precision (\(P\)), and normalized precision (\(P_{norm}\)) to measure the performance. **GOT-10k. ** GOT-10k [17] is a large-scale short-term tracking dataset comprising over 10,000 video clips of moving objects in real-world scenarios. The object categories in the training set and test set are completely non-overlapping. The test set contains 180 videos, covering various challenging scenarios common in tracking. We strictly follow official requirements to train our tracker only on the GOT-10k training split and submit the tracking results to the official evaluation server. It can be seen from Table 2 that the proposed FETrack-256 achieves the optimal performance in two tested metrics. For example, our FETrack-256 improves the AO metric by 2.2% compared with VideoTrack and improves the \(SR_{0.75}\) metric by 3.5% compared with TATrack. This indicates that our FETrack is able to identify and locate the target more accurately. **LaSOT. ** LaSOT [18] is a large-scale long-term tracking dataset that covers 1400 video sequences, of which the test set contains 280 videos with an average length of 2448 frames. As can be seen from Table 2, our FETrack-256 slightly outperforms the latest ARTrack-256. With the same ViT-B encoder architecture and input resolution, the proposed FETrack-256 achieves a 0.2% and 0.4% improvement over ARTrack-256 in terms of the AUC score and \(P_{norm}\) metric, respectively. In addition, the high-resolution version of FETrack-384 obtains an AUC score of 71.8%, outperforming all comparison trackers. These results indicate that our FETrack is capable of improving stability and accuracy in long-term tracking scenarios. **LaSOT\({}_{ext}\). ** LaSOT\({}_{ext}\)[20] is a recently released dataset extending LaSOT, which contains 150 extra video sequences from 15 object categories. Due to the existence of many similar interfering videos, LaSOT\({}_{ext}\) is more challenging than LaSOT. From Table 2, we can see that our FETrack-256 is superior to state-of-the-art trackers. For example, our FETrack achieves 61.3% in the \(P_{norm}\) metric, 0.5% higher than SeqTrack. Moreover, our FETrack-384 significantly outperforms the comparison trackers on all three tested metrics. The above results indicate that our FETrack not only possesses a significant generalization capability but also a strong anti-interference capability.