# Object Tracking Algorithm Based on Multi-Layer Feature Fusion and Semantic Enhancement
## 2 Method
### Construction of Object Semantic Enhancement Model
#### (1) Cross-attention learning
The model learns the dynamic relationship between template images and search images through the cross-attention mechanism, which enables the model to dynamically adjust its attention based on real-time changes in the object and background. In complex scenes, especially when the object is surrounded by similar objects, the model can update its understanding of the object in real time, thereby more effectively resisting the clutter of similar objects. Feature fusion strategy  
The search features fused through Layer2 and Layer3 can integrate information from different network depths, including local details and global semantics. This integration strategy provides rich and comprehensive representation information for the model, enabling the model to better understand the essential attributes of the object, including shape, size, texture, etc., thereby improving the accuracy of object recognition. The output of the search feature \(X_{2}^{\prime}\) from Layer2 and the output of the search feature \(X_{3}^{\prime}\) from Layer3 are merged, and the main output process is shown in Equations (5)-(8). \[S_{1}\big{(}X_{3}^{\prime}\big{)}=norm\big{(}X_{3}^{\prime}+dropout\big{(}X_{2}^{\prime}\big{)}\big{)} \tag{5}\]  
\[S_{2}\big{(}X_{3}^{\prime}\big{)}=relu\big{(}linear\big{(}S_{1}\big{(}X_{3}^{ \prime}\big{)}\big{)}\big{)} \tag{6}\]  
\[S_{3}\big{(}X_{3}^{\prime}\big{)}=linear2\big{(}dropout\big{(}S_{2}\big{(}X_{3}^{\prime}\big{)}\big{)}\big{)} \tag{7}\]  
Figure 3:0By leveraging linear layers, activation layers, and normalization layers, the model is capable of effectively integrating features from various levels. The application of dropout operations aids in preventing model overfitting and enhances its generalization capability across different scenarios. Ultimately, cross-attention correlation is conducted between \(X_{3}^{\prime\prime}\) and the template feature \(Z_{3}^{\prime\prime}\), which refines the model's attention allocation toward the object. This process ensures that the attention is more concentrated on the object's key areas, thereby improving the model's accuracy in predicting the object's position. The resulting search features are then utilized for the final prediction task. Feature visualization  
In this paper, the model is named Transtfpn, based on TransT, which integrates multi-layer semantic information. Due to the problem of similar background clutter between the Basketball and Bird video sequences in the OTB-100 dataset, in order to visually present the effect of multi-layer feature fusions, for the first frame images of the Basketball and Bird video sequences in the OTB-100 dataset, the multi-level attention feature maps of TransT's Layer3 feature and the multi-level attention feature maps of Transtfpn's fusion feature are visualized in the form of heat maps in this paper. This is shown in Figure 4, where N represents the number of stacked cross-attention modules. As shown in Figure 4, when N = 4, the model is able to effectively synthesize attention information from multiple levels and boost its feature representation capability. Moreover, it is evident that the results of TransT are rather dispersed, lacking sufficient focus on the object itself. The model's grasp of the object's state is not detailed enough, and the distribution of influence values is more susceptible to background factors. The results of Transfpn are more accurate in both positioning and object range locking. More importantly,  
Figure 3:1 Visual comparison of attention feature maps between Transtfpn and TransT at different values of N (N represents the number of stacked cross-attention modules). it can concretize the actual shape of the object, which has a great promoting effect on distinguishing between objects and similar objects. Therefore, the model can improve judgment accuracy in the tracking process through this learning.