# Object Tracking Algorithm Based on Multi-Layer Feature Fusion and Semantic Enhancement
## 3 Experimental Results and Analysis
### Analysis of Experimental Results
#### 3.3.2 Quantitative Analysis
By enhancing appearance semantics, the model is able to focus more effectively on an object's key features, resulting in improved adaptability and accuracy in complex environments. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Algorithm Name**} & \multicolumn{2}{c}{**OTB-100**} & \multicolumn{2}{c}{**LaSOT**} \\ \cline{2-5}  & **Norm Precision** & **Success** & **Norm Precision** & **Success** \\ \hline TransT & 0.752 & 0.564 & 0.544 & 0.473 \\ Transtfpn & 0.762 & 0.587 & 0.550 & 0.475 \\ Transtfpn-Mask & 0.801 & 0.624 & 0.653 & 0.569 \\ Transtfpn-T & 0.860 & 0.654 & 0.660 & 0.580 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of tracking results between TransT and the proposed algorithm in background clutter scenes. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Tracking Algorithm** & **Norm Precision** & **Success** \\ \hline Transtfpn-T & 0.885 & 0.687 \\ TransT & 0.876 & 0.681 \\ TaTrack [17] & - & 0.641 \\ SiamRPN++ [5] & 0.804 & 0.611 \\ SiamFC [1] & 0.694 & 0.468 \\ ViTCRT [28] & - & 0.686 \\ TriSiam [16] & 0.865 & 0.663 \\ SiamAttn [29] & 0.845 & 0.650 \\ SiamTPN [13] & 0.823 & 0.636 \\ HCAT [30] & 0.812 & 0.627 \\ DiMP [31] & 0.849 & 0.642 \\ E.T.Track [32] & - & 0.623 \\ TSN [33] & 0.791 & 0.613 \\ HSET [34] & 0.762 & 0.544 \\ ABTrack [35] & 0.823 & 0.652 \\ HiFT [14] & 0.787 & 0.589 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: A performance comparison of the proposed algorithm in this paper with other algorithms on the UAV123 dataset. As presented in Table 7, this study conducted evaluations on the long-term tracking dataset LaSOT. In addition to the methods discussed in this paper, comparisons were made with several well-established object tracking algorithms, including SiamFC, SiamRPN++, SiamMask, and DiMP, as well as Transformer-based tracking algorithms such as SiamTPN, HCAT, and TrSiam. This paper used a curve graph to plot the comparative tracking results of all the algorithms mentioned above, as shown in Figure 8.  
Transftpn-T, proposed in this paper, surpassed the average accuracy of the classic fully convolutional Siamese networks SiamFC, SiamMask, and SiamRPN++ by 31.8%, 18.6%, and 16.8%, respectively, and the success rate increased by 31.3%, 18.2%, and 15.4%; Compared to Transformer-based tracking algorithms, there was also a 2% to 6% improvement in the average accuracy and success rate. Compared to the classic fully convolutional Siamese network algorithms, Transtfpn-T, proposed in this paper, applies a self-attention mechanism, allowing the algorithm to consider all elements in the input sequence when processing each element. This integration of global information is particularly important for tracking, as it enables the model to better understand the relationship between the object and the background, as well as the interaction between objects, thereby greatly improving the accuracy of tracking. Therefore, it has better adaptability and generalization ability than fully convolutional Siamese networks in tracking results. This paper further optimizes the model based on the scalability of the Transformer to ensure that it can adapt to various tracking tasks and improves the algorithm's understanding of deep semantics and contextual information. Therefore, the algorithm proposed in this paper performs more accurately and robustly in handling complex video sequences and dynamically changing environments.