# Object Tracking Algorithm Based on Multi-Layer Feature Fusion and Semantic Enhancement
## 2 Method
### Construction of Object Semantic Enhancement Model
#### 2.1.3 An Object Enhancement Model Integrating Mask Features
We label the mask of the retained feature point with \(M_{i}=1\) and the mask of the removed feature point with \(M_{i}=0\). During calculation, the feature influence value of the feature points whose mask is equal to 0 is 0, and the feature influence value of the feature points whose mask is equal to 1 is unchanged. The feature influence value \(V_{i}\) of each feature point after applying the mask is calculated as shown in Equation (9). \[V_{i}=v_{i}\cdot M_{i} \tag{9}\] \begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature Points** & **Precision (Pre)** & **Success (Suc)** \\
**Retention Rate** & & \\ \hline
40\% & 0.886 & 0.677 \\
50\% & 0.892 & 0.681 \\
60\% & 0.888 & 0.689 \\
70\% & 0.895 & 0.686 \\
80\% & 0.887 & 0.679 \\
90\% & 0.690 & 0.479 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The accuracy and success rate of the model when retaining different rates of high feature influence values. Figure 5: Feature mask generation and processing process (taking Layer3 as an example). Finally, the feature points after applying the mask are formed into a new search feature, \(X\prime\), according to their original positions. After the mask strategy is applied, the influence values of the feature inside the search feature are updated. The updated search features are used for the next cross-attention learning. The search features after each cross-attention learning dynamically generate masks based on the updated feature influence values, ensuring that the model only focuses on the features labeled as important in subsequent processing. This paper visualizes the feature influence value distribution diagram of the search feature after each cross-attention learning; the masks were dynamically generated based on the feature influence value distribution, and the feature influence value distribution maps after the application of the masks are shown in Table 3 (taking Layer3 features as an example). From Table 3, it can be observed that by applying feature masks after cross-attention learning in each layer, the most relevant features to the object can be effectively highlighted. The feature mask enhances the semantic understanding ability of the model, and by filtering out the most influential features, it can better support the model for effective semantic enhancement and object recognition in various complex environments. Simultaneously optimizing attention allocation, by optimizing at the feature level, i.e., retaining only a certain proportion of high-impact value features, feature masks help the model more accurately locate objects among numerous candidate regions. This optimized attention allocation mechanism can effectively handle the problem of similarity clutter in tracking, as it ensures that the model's attention is more focused on the true object. \begin{table}
\begin{tabular}{c c c} \hline
**Map of Feature Influence** & **Feature Influence Value** \\
**Values Distribution of** & **Generated Mask Map** & **Distribution Map After** \\
**Search Feature** & & **Mask Application** \\ \hline The output of the first & & \\ cross-attention & & \\ \hline The output of the second & & \\ cross-attention & & \\ \hline The output of the third & & \\ cross-attention & & \\ \hline The output of the fourth & & \\ cross-attention & & \\ \hline \end{tabular}
\end{table} 
Table 3: A visualization of the effect of mask application on each cross-attention search feature.