# Object Tracking Algorithm Based on Multi-Layer Feature Fusion and Semantic Enhancement
## 4 Summary  
This paper proposes an object tracking algorithm that includes a Transformer attention mechanism. To address the problem of boundary box drift in the model when objects with the same texture or appearance appear around the object, a hierarchical feature fusion method is proposed to enhance semantic information and focus on the temporal feature information of the object. By using the features of Layer2 and Layer3 in ResNet50, information about the object at different levels of abstraction can be captured. This fusion \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Factors**} & \multirow{2}{*}{**Algorithm Name**} & \multicolumn{2}{c}{**OTB-100**} & \multicolumn{2}{c}{**LaSOT**} & \multicolumn{2}{c}{**UAV123**} \\ \cline{3-8}  & & **Pre** & **Suc** & **Pre** & **Suc** & **Pre** & **Suc** \\ \hline \multirow{2}{*}{Fast motion} & TransT & **0.871** & 0.662 & 0.434 & 0.373 & 0.860 & 0.656 \\  & Transtfpn-T & 0.866 & 0.684 & 0.583 & 0.505 & 0.863 & 0.658 \\ \hline \multirow{2}{*}{Motion blur} & TransT & **0.883** & 0.684 & 0.562 & 0.487 & - & \\  & Transtfpn-T & 0.851 & 0.685 & 0.713 & 0.623 & - & \\ \hline \multirow{2}{*}{Deformation} & TransT & 0.852 & 0.606 & 0.640 & 0.559 & - & \\  & Transtfpn-T & 0.877 & 0.658 & 0.754 & 0.670 & - & \\ \hline \multirow{2}{*}{Illumination variation} & TransT & 0.837 & 0.638 & 0.609 & 0.524 & **0.816** & **0.617** \\  & Transtfpn-T & 0.841 & 0.673 & 0.775 & 0.675 & 0.808 & 0.612 \\ \hline \multirow{2}{*}{Low resolution} & TransT & 0.808 & 0.586 & 0.506 & 0.431 & 0.772 & 0.542 \\  & Transtfpn-T & 0.956 & 0.714 & 0.651 & 0.564 & 0.794 & 0.558 \\ \hline \multirow{2}{*}{Occlusion} & TransT & 0.814 & 0.605 & - & & - & \\  & Transtfpn-T & 0.851 & 0.656 & - & & - & \\ \hline \multirow{2}{*}{Out of view} & TransT & **0.850** & **0.632** & 0.512 & 0.439 & 0.857 & 0.663 \\  & Transtfpn-T & 0.718 & 0.566 & 0.656 & 0.575 & 0.867 & 0.672 \\ \hline \multirow{2}{*}{Scale variation} & TransT & **0.896** & 0.672 & 0.621 & 0.538 & 0.860 & 0.667 \\  & Transtfpn-T & 0.889 & 0.694 & 0.737 & 0.647 & 0.870 & 0.674 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of results between TransT and the proposed algorithm under other influencing factors. strategy helps the model better understand and distinguish subtle differences between the object and the background or similar objects. By dynamically adding masks, the model's attention can be more focused on the object itself, reducing the negative impact of similar objects or complex backgrounds on tracking performance. Finally, an update module is added at the end of the network to reduce tracking loss or false tracking caused by background clutter. The experiment found that the improvement proposed in this paper not only significantly improves the tracking performance of the model in scenarios with background clutter but also significantly improves the overall tracking performance of the model. However, experimental results reveal that the proposed algorithm performed variably across different scenarios in different datasets. For instance, its performance was relatively poor in scenarios such as motion blur, scale variation, and fast motion within the OTB-100 dataset, while it demonstrated better effectiveness in other scenarios and on the LaSOT and UAV123 datasets. Moreover, compared with other Transformer-based object tracking algorithms such as STARK, SwinTrack, and MixFormer, the performance of the proposed method was still inferior. Therefore, future improvements to the algorithm can be made in the following aspects:  
1. Introducing an occlusion detection mechanism to dynamically control the frequency and content of template updates, enabling the model to better adapt to various scenarios and enhance robustness. 2. Incorporating temporal contextual information to improve the model's ability to recognize objects when they temporarily go out of view. 3.