# 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  
This paper tested the original TransT algorithm and the improved algorithms Transtfpn, Transtfpn-T, and Transtfpn-Mask based on it on the public datasets OTB-100 and LaSOT. The algorithm was objectively evaluated by evaluating the accuracy and success rate values of the evaluation indicators, as shown in Table 4.  
When comparing the Transtfpn algorithm to the TransT algorithm, the success rate on the OTB-100 dataset saw an increase of 0.9%, while the accuracy on the LaSOT dataset improved by 1.1% and the success rate by 0.4%. This study demonstrates that combining features from Layer2 and Layer3 allows the model to capture both rich semantic information and finer spatial details simultaneously. This fusion of hierarchical features enhances the model's ability to distinguish between objects and backgrounds, thereby improving tracking accuracy. The Transtfpn-Mask algorithm, which incorporates dynamic masks based on Transtfpn, showed significant improvements over the TransT algorithm. On the OTB-100 dataset, it achieved a 3.7% increase in accuracy and a 5% increase in the success rate. On the LaSOT dataset, the average accuracy improved by 9.4% and the success rate by 7.9%. The addition of masks helps reduce background clutter and enhances focus on the object, thereby improving tracking accuracy and robustness. The Transtfpn-T algorithm, which integrates temporal information into Transtfpn-Mask, further enhanced performance. Compared to the TransT algorithm, it achieved a 4.3% increase in accuracy and a 4.5% increase in the success rate on the OTB-100 dataset. On the LaSOT dataset, the average accuracy improved by 11.4% and the success rate by 10.8%. The incorporation of temporal information fusion makes the algorithm more robust in handling scenarios such as rapid object movement, occlusion, and lighting changes, thereby improving overall tracking performance. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multirow{2}{*}{**Depth Features**} & \multirow{2}{*}{**Template Type**} & \multicolumn{2}{c}{**OTB-100**} & \multicolumn{2}{c}{**LaSOT**} \\ \cline{3-6}  & & & **Pre** & **Suc** & **Pre** & **Suc** \\ \hline TransT & Layer3 & Initial frame & 0.851 & 0.639 & 0.624 & 0.541 \\ Transfpn & Layer2 + Layer3 & Initial frame & 0.848 & 0.648 & 0.635 & 0.545 \\ Transtfpn-Mask & Layer2 + Layer3 & Initial frame + update frame & 0.888 & 0.689 & 0.718 & 0.620 \\ Transfpn-T & Layer2 + Layer3 & Initial frame + update frame & 0.894 & 0.684 & 0.738 & 0.649 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of tracking results between the algorithm proposed in this paper and other algorithms on the UAV123 dataset. In addition, using the UAV123 dataset, this paper compared the final algorithm Transfpn-T with other object tracking algorithms, such as ViTCRT [28], SiamAttn [29], HCAT [30], DiMP [31], etc. The results are shown in Table 5.  
As can be seen from Table 5, compared to some classic Transformer-based object tracking algorithms, Transtfpn-T has the highest success rate and the best tracking performance. The reason lies in the effective combination of multi-layer feature fusion, the dynamic masking strategy, and the template update module. These innovative methods enable algorithms to have higher tracking accuracy and robustness in complex scenes, especially when dealing with small-object scenes and similar-object clutter problems, showing significant advantages. The issue under investigation was evaluated using the OTB-100 and LaSOT datasets in this study. Table 6 illustrates the video sequence of the object in a background clutter scenario. By comparing the results of anti-similar background clutter performance, Transtfpn, Transtfpn-Mask, and Transtfpn-T improved the success rates by 2.3%, 6%, and 9%, respectively, on the OTB-100 dataset and increased the success rate values by 0.2%, 9.6%, and 10.7% on the LaSOT dataset. The proposed algorithm in this paper achieves a deep understanding and accurate tracking of objects.