# Object Tracking Algorithm Based on Multi-Layer Feature Fusion and Semantic Enhancement
## 1 Introduction
The dual-branch CNN-Transformer network TabCtNet [20] applies an object-aware erasure strategy, generating attenuated heatmaps from template masks in a data-driven manner to reduce the impact of similar distractors, and designs a corner-based pixel-level refinement module to extract finer-grained spatial information, achieving more accurate bounding box estimation and mitigating the effects of object deformation. TransT [21], as a representative approach that combines Siamese networks with Transformer mechanisms, makes full use of cross-attention modules to model the interaction between the template and the search region, effectively overcoming traditional Siamese networks' limitation in capturing global dependencies. TransT was tested on the OTB100 dataset, with the success rates (Suc) under various challenging factors presented in Table 1. The full names of each factor are introduced in Section 3.2 of this paper. As observed from Table 1, the success rate in background clutter (BC) scenarios is relatively low, with the highest number of failures. Background clutter refers to scenes where the background contains colors or textures similar to the object, which may cause the tracker to mistakenly identify background regions as the object, leading to tracking failure. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Factors** & **IV** & **SV** & **OCC** & **DEF** & **MB** & **IPR** \\ \hline Suc (\%) & 63.8 & 67.2 & 62.5 & 60.6 & 68.4 & 64.5 \\ \hline
**Factors** & **OPR** & **OV** & **BC** & **LR** & **FM** & \\ \hline Suc (\%) & 62.8 & 63.2 & 56.4 & 58.6 & 66.2 & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The success rate of TransT under 11 tracking factors on the OTB100 dataset. Through an in-depth analysis of the TransT network structure, it was observed that while its main strength lies in the comprehensive feature fusion enabled by the Transformer, it lacks detailed information necessary for accurately distinguishing and localizing the object and background. In addition, it does not effectively utilize the multi-layer outputs of the backbone network to perform a thorough analysis of scene content. Furthermore, the absence of temporal information integration to accommodate changes in the object's appearance and position leads to unstable tracking results, thereby limiting the algorithm's robustness and applicability. Therefore, this paper performs the following work to solve these problems:  
1. The object semantic enhancement model is constructed by using a multi-layer feature fusion method. Specifically, multi-layer features are fused through the cross-attention mechanism to ensure that the detailed description of the object is associated with the background context and enhance the expression ability of the search features. 2. The dynamic mask strategy is proposed. We introduce dynamic masks into the output features of cross-attention, further focusing on object information and significantly reducing the impact of background clutter. 3. The object template update mechanism guided by the judgment conditions [22] is employed. In particular, when the prediction result of the current frame exhibits high correlation with the adjacent two frames, a new template image is generated by cropping around the current predicted frame. This updated template is then fed back into the network input, enabling the template to better adapt to appearance variations in the object and changes in the scene background throughout the video sequence, thus maintaining tracking effectiveness over time.