# Object Tracking Algorithm Based on Multi-Layer Feature Fusion and Semantic Enhancement
## 3 Experimental Results and Analysis
### Analysis of Experimental Results
#### 3.3.1 Qualitative Analysis  
As shown in Figure 7, this paper tested video sequences containing background clutter factors (including similarity clutter) on the OTB-100 dataset, including (a) Bolt, (b) Deer, (c) Football, (d) Liquor, (e) Matrix, and (f) Soccer. The focus was on testing the original TransT algorithm and the improved algorithm based on it to visualize the bounding box of the prediction results. In the (a) video sequence, there is always an object with consistent texture, color, and appearance around the object. It was found that even with the fusion of hierarchical features, there was still deviation in the later stage of tracking. However, after using the template update module and mask module, the model could achieve stable tracking, indicating that the initial information was not sufficient to cope with changes in scene information, and key guidance information needed to be obtained in a timely manner from the intermediate process. In the (b) video sequence, TransT could not focus its attention well on the parts to be focused on but instead classified all non-focused parts around the object as prediction results. In contrast, the algorithm presented in this paper concentrated solely on the regions of interest, and its localization accuracy remained unaffected even when encountering objects with similar appearances. This demonstrated the algorithm's strong capability in capturing the distinctive shape characteristics of the object.  
In the (c) video sequence, there are many similar objects around the object, and TransT, Transtfpn, and Transtfpn-Mask all exhibited boundary box drift. Only Transtfpn-T could adapt well to environmental changes during the tracking process, because the model can make good use of temporal information to adapt to the shape and spatial position changes in an object during the process and give higher influence to the information near the center of the object, making the model always stable in complex scenes. In the (d) video sequence, both TransT and Transtfpn exhibited varying degrees of bounding box deviation. Transtfpn-Mask was subject to clutter during object movement, and Transtfpn-T predicted the range most accurately. It is evident that by consistently updating the object's latest  
Figure 7: Performance comparison of the proposed algorithms in this paper and TransT on the OTB-100 dataset.  
state and narrowing down the scope of object features to a certain extent, the system could adapt to the object's continuous positional changes and mitigate the clutter of background information. The biggest problem with TransT in the (e) video sequence was its inability to adapt to rapidly changing scenes and achieve precise positioning, resulting in boundary box drift. After integrating multiple layers of features, it is already able to adapt to most scene transformations, while incorporating temporal information and concentrating feature attention processing results in adapting to all scene transformations. In the (f) video sequence, the object frequently disappears and appears, and as the perspective changes, the number of similar objects around the object gradually increases. Both TransT and Transtfpn could not adapt well to this complex scene, and Transtfpn-Mask experienced a brief boundary box drift phenomenon. After introducing temporal information, even if the object disappeared in the image for a period of time, the Transtfpn-T model could continue to accurately locate the object in the image where it reappeared. The analysis of the scene was relatively accurate, and it could distinguish the object from similar objects around it regardless of the perspective.