## 4 Algorithm Test Results
### OTB Dataset Test Results and Analysis  
The OTB dataset [6] is one of the most widely used benchmark datasets for visual object tracking. The OTB datasets include OTB-2013, OTB-50, and OTB-100. The OTB-100 dataset consists of 100 video sequences containing 11 complex situations such as deformation, occlusion, fast motion, and illumination changes. Therefore, we validated our proposed algorithm on the OTB-100 dataset. We evaluated the Siamese network single-object tracking algorithm that incorporates multiple attention mechanisms with adaptive updating strategies for background features by two evaluation metrics, precision and success rate, and plotted the precision and success rate graphs for these two metrics to compare with other algorithms. At the same time, we view the tracking effect of our proposed algorithm and compare it with other algorithms. The specific meanings of the two evaluation metrics, precision and success rate, respectively, are the ratio of the number of successful frames to the total number of frames for which the Euclidean distance between the tracking result and the labeled center position of the datasets is less than a certain threshold, representing the precision. The ratio of the number of frames to the total number of frames for which the tracking frame coverage is greater than a certain threshold represents the success rate, in which the coverage rate is defined as the ratio of the area of the intersection portion to the area of the concatenation portion between the predicted tracking frames and labeled tracking frames. As shown in Figure 6, our proposed algorithm achieved the leading level in the comparison experiments with SRDCF [15], SAMF [38], MEEM [39], CSK [13], DSST [40], SiamFC [20], SiamRPN [22], and SiamFC++ [23] on the OTB-100 dataset, with a tracking precision of 0.818 and a tracking success rate of 0.610. Our proposed algorithm ran at an average speed of more than 60 fps in the experiments. Compared with the benchmark framework algorithm SiamFC, our proposed algorithm improves the precision by 7% and the success rate by 5.3%, which indicates that the Siamese network single-object tracking algorithm integrating multiple attentional mechanisms and an adaptive updating strategy of background features can effectively cope with the problem of degradation of tracking performance during the process of tracking an object in complex environments, and further improve the tracking precision and precision. occlusion, scale changes, and other factors, which lead to large changes in the target appearance representation model, making it difficult to track a moving target. In order to improve the model's adaptability and separability to complex backgrounds, multiple attention mechanisms are introduced. The attention mechanisms allow the algorithmic model to dynamically focus on specific regions of the input data, thus improving processing efficiency and precision of results. In target tracking, the attention mechanism can help the model better understand the image content and distinguish between different targets, especially in the case of overlapping or occluded targets. By introducing the attention mechanism, discriminative features can be effectively extracted and important features in the target can be focused on, thus improving the precision and robustness of target tracking. Figure 7: Comparison of the precision of this algorithm with other algorithms for 11 different complex cases in the OTB-100 dataset. Figure (**a**) represents the precision of this algorithm with respect to other algorithms at low resolution properties. Figure (**b**) represents the precision of this algorithm versus  
other algorithms with respect to the background blurring property. Figure (**c**) represents the precision of this algorithm versus other algorithms for out-of-field-of-view attributes. Figure (**d**) represents the precision of this algorithm with respect to other algorithms for the in-plane rotation property.