# AR Long-Term Tracking Combining Multi-Attention and Template Updating
## 4 Experimental Analysis
### Analysis of Results  
#### 4.2.1 Quantitative Analysis  
OTB100. The OTB100 dataset is a widely used benchmark for evaluating the performance of object-tracking algorithms in computer vision. It contains 100 video sequences of different object categories, such as pedestrians, vehicles, and animals, in various scenarios, such as in-the-wild, urban, and indoor environments. The video sequences vary in terms of object size, camera motion, object motion, and occlusions, providing a comprehensive and challenging evaluation for object-tracking algorithms. The dataset provides ground truth annotations for the objects' location, making it easier to quantify the accuracy of tracking algorithms. The OTB100 dataset has become a standard benchmark in the field of object tracking and is widely used by researchers and developers to evaluate and compare the performance of different tracking algorithms. OTB100 dataset is a widely used benchmark for evaluating the performance of object-tracking algorithms in computer vision. It has 100 sequences, including different object categories, such as pedestrians, vehicles, and animals, in various scenarios, such as in-the-wild, urban, and indoor environments. In addition, there are hand-marks with nine attributes on the test sequences to represent challenging aspects, for example, object size, camera motion, object motion, and occlusions, providing a comprehensive and challenging evaluation of object-tracking algorithms. Two evaluation indicators are used, an accuracy score and a successful score. The accuracy score is the percentage of frames with the true distance between the center of the tracking result and the ground below 20 pixels. The success rate graph shows the percentage of successfully tracked frames at different thresholds. We compared with other public algorithms in the OTB100 data set, and the experimental results are shown in Table 1 and Figure 7. Table 1 and Figure 7 show that our tracker achieves a success of 69%, which is 10.3% higher than SiamFC and 6.1% higher than SiamRPN. As we can see from Table 1 and Figure 7, the success rate of our method is 0.690, and the accuracy rate is 0.889. The success rate is 15.6% higher than SiamFC and 11.4% higher than SiamRPN, and the accuracy rate is 14.8% higher than SiamFC and 7.3% higher than SiamRPN. Several video sequences were selected to test the performance of the algorithm under different attributes such as occlusion, deformation, and motion blur, to name a few. These attributes are common challenges in the field of target tracking, which can lead to changes in the shape, position, and other features of the target or reduce the difference between the target and the background, thus affecting the tracking results. The experimental results are shown in Tables 2 and 3. The best three results are marked in red, green, and blue bold fonts, respectively. Tables 2 and 3 indicate that our tracker can achieve the top three performances under most of the attributes. LaSOT. LaSOT is a large-scale dataset for object tracking. It contains a total of 1400 high-resolution sequences, of which 1120 are for training and 280 are for testing. We compared the success rate (Success) and accuracy (P,P\({}_{\text{Norm}}\)) scores of various advanced trackers on LaSOT datasets. As shown in Figure 8 and Table 4, our tracker obtains a success of 65.1%, which is 13.6% higher than DaSiamRPN and 15.6% higher than SiamRPN++. At the same time, we compare the success for different attributes in LaSOT, such as ROT (the target rotates in the image), DEF (the target is deformable during tracking), IV (the illumination in the target region changes), OV (the target completely leaves the video frame), SV (the ratio of bounding box is outside the range), BC (the background has a similar appearance to the