# FETrack: Feature-Enhanced Transformer Network for Visual Object Tracking
## 5 Conclusions  
To tackle the template feature quality degradation of the one-stream tracker due to unrestrained feature interaction, we propose FETrack, a feature-enhanced transformer-based network for visual object tracking. First of all, we embed an independent template stream in the one-stream encoder to prevent the templates from potential interferences from cluttered search regions. Secondly, we adopt a sequence-learning-based causal transformer in the decoder to generate the target bounding box in an autoregressive manner, which greatly simplifies the tracking framework. In addition, we separately design a dynamic threshold-based online template-updating strategy and a template-filtering approach, which prompt our FETrack to effectively handle complex scenes, such as those with scale variation, occlusion, and out-of-view objects. Extensive experiments show that the proposed FETrack is superior to state-of-the-art trackers. Nevertheless, while the proposed FETrack utilizes dynamic templates to integrate the information of historical frames, it still faces challenges in dealing with prolonged occlusion and targets out of view. In our future work, we will focus on exploring a more effective template-update strategy by comprehensively considering factors such as appearance changes, key features of the target, and dynamic thresholds. Moreover, we will address the computational overhead issue while maintaining the tracking accuracy, enabling the proposed FETrack to be deployed on embedded devices with limited computational resources.  
Figure 8: Comparison of the success rate with state-of-the-art trackers on eleven challenging attributes, including Illumination Variation, scale variation, occlusion, Deformation, Motion Blur, fast motion, In-Plane Rotation, Out-of-Plane Rotation, out of view, background clutter, and Low Resolution, and all attributes (ALL) in the OTB100 dataset. Where different colored lines represent the success rate obtained by different trackers in the corresponding attribute, and the value under the attribute represents the best success rate obtained by all trackers in this attribute  
**Author Contributions:** Conceptualization, H.L.; methodology, H.L. and D.H.; software, H.L.; validation, H.L. and M.L.; formal analysis, H.L.; writing--original draft preparation, H.L.; writing--review and editing, H.L. and D.H.; visualization, H.L. and M.L.; funding acquisition, D.H. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported in part by the National Key R&D Program of China under grant 2021 YFE0205400, in part by the National Natural Science Foundation of China under grant 61901183 and grant 61976098, in part by the Fundamental Research Funds for the Central Universities under grant ZQN-921, in part by the Collaborative Innovation Platform Project of Fujian Province under grant 2021 FX03, in part by the Natural Science Foundation of Fujian Province under grant 2023J01140, in part by the Key Project of Quanzhou Science and Technology Plan under grant 2023C007R, and in part by the Key Science and Technology Project of Xiamen City under grant 3502Z20231005.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study. However, if you need the code of the relevant module, you can contact the author by email: [EMAIL_ADDRESS]  
**Conflicts of Interest:** The authors declare no conflicts of interest.