# Object Tracking Algorithm Based on Multi-Layer Feature Fusion and Semantic Enhancement
## 3 Experimental Results and Analysis
### Analysis of Algorithm Limitations  
The comparative experimental results indicate that the proposed algorithm can still accurately track an object even in the presence of similar background interference, thereby verifying its robustness in complex environments. To further evaluate the generalization capability and practical value of the algorithm in real-world complex scenarios, this paper conducted tests on the representative OTB-100, LaSOT, and UAV123 datasets for scenarios such as rapid movement, occlusion, and illumination changes. Among them, the OTB-100 dataset represents classic tracking scenarios in real-world settings; the LaSOT dataset features longer sequences and richer interference factors; and the UAV123 dataset focuses on aerial video scenarios captured from UAV platforms, characterized by large scene variations, small object sizes, and frequent occlusions. The corresponding experimental results are shown in Table 8. Among them, \"-\" indicates that there is no corresponding scenario in the dataset for experimental testing.  
From the table, it can be seen that the proposed algorithm demonstrated strong tracking performance across multiple representative datasets. In the LaSOT dataset, the proposed algorithm significantly outperformed TransT in both tracking accuracy and the success rate under all testing conditions, verifying its robustness and stability in long-term and complex scenarios. It showed particularly strong adaptability when dealing with challenges such as fast motion, motion blur, and illumination changes. On the OTB-100 dataset, the proposed algorithm generally achieved higher tracking accuracy and success rates than TransT but showed slight performance degradation in certain cases such as fast motion, motion blur, out-of-view scenarios, and scale variation: under fast motion, the average accuracy was 0.005% lower than that of TransT; under motion blur, it was 0.032% lower; for out-of-view scenarios, the average accuracy and success rate were 0.132% and 0.066% lower, respectively; and under scale variation, the average accuracy was 0.007% lower. This can be attributed to the algorithm's suboptimal responsiveness in sudden  
Figure 8: Graph comparing the results of the algorithms involved in this paper with other algorithms on the LaSOT dataset. (**a**) Norm precision; (**b**) success rate value.  
and severe change scenarios, particularly when an object undergoes rapid morphological changes or temporary disappearance within a short timeframe. The model's adaptability to such feature variations still requires improvement. On the UAV123 dataset, the proposed algorithm outperformed TransT in most scenarios in terms of both tracking accuracy and the success rate, indicating its advantage in identifying and tracking objects from aerial viewpoints and at long distances. However, under illumination variation, there was a slight decline, with the average accuracy and success rate being 0.008% and 0.005% lower than those of TransT, respectively. This was due to the dramatic lighting changes affecting the matching accuracy between the feature template and the current object.  
Overall, the proposed algorithm exhibited strong robustness and generalization capability in addressing diverse tracking challenges such as fast motion, low resolution, and scale variation, with particularly outstanding performance on more challenging datasets like LaSOT and UAV123, fully validating its effectiveness. However, the algorithm still showed limitations in handling abrupt changes such as motion blur and out-of-view situations on OTB-100 dataset. Future improvements may include developing a more efficient motion prediction module to enhance responsiveness and stability under rapidly changing scenarios; further optimizing the template update strategy to better adapt to variations in illumination, scale, and viewpoint; and integrating temporal context information to strengthen recognition capabilities in out-of-view cases.