## 4 Algorithm Test Results
### OTB Dataset Test Results and Analysis
The tracking algorithm tracks only the objects in the video sequence that are bounded by a boundary; the initial position of the target is given in the first frame of the video sequence and the tracking of the subsequent frames is done by the tracking algorithm. In the Biker video sequence, [PERSON]'s face is tracked. In the Bird1 video sequence, a bird is tracked. In the Baby Dragon video sequence, the tracking is of [PERSON]'s face. In the freeman4 video sequence, it is [PERSON]'s face that is tracked. In the \"Kitesurfing\" video sequence, the tracked is the face of a \"kitesurfer\". In the \"Motor rolling\" video sequence, the tracking is of bikes and cyclists. In the Triathlon video sequence, tracked is the face of [PERSON]. In the jumping video, tracked are high jumpers. Running speed is an important evaluation index of target tracking algorithms. This algorithm has an average running speed of 60 FPS when testing the OTB100 dataset on a computer with a CPU of Intel(r) Xeon(r) E5-2696 and a GPU of NVIDIA RTX3080 TI 16 G and compares it with the running speeds of the other six tracking algorithms, and the results are shown in Table 2. From the table, it can be seen that this algorithm has a large advantage in running speed compared with MEEM and SAMF tracking algorithms, which is due to the fact that this algorithm trains a similarity matching model offline through  
Figure 9: Tracking results of this algorithm with other algorithms for 9 video sequences in the OTB-100 dataset. the training dataset, and there is no online updating process; there is a large gap in the running speed compared with CSK tracking algorithm, which is due to the large amount of computation in the twin network; and there is a large gap in the running speed compared with the benchmark algorithm framework SiamFC, the running speed of this algorithm decreases to a certain extent due to the deeper backbone feature extraction network, but it can still meet the requirement of real-time tracking. To verify the effectiveness of the four modules proposed in our algorithm, namely, the deep backbone feature extraction network with a small convolutional kernel fused with jump-layer connected features, an adaptive updating strategy for background features, multiple attention mechanisms, and feature response fusion operation after convolutional inter-correlation. We designed three ablation experiments, Siam_SHD, Siam_SHD_BAUS, and Siam_SHD_BAUS_MAM, to evaluate the performance impact of these four components on our algorithms through ablation experiments using SiamFC and our algorithms as a reference benchmark. Siam_SHD uses a deep backbone of small convolution kernel fused with a hopping-layer connected feature extraction network to replace the tracking algorithm of the shallow AlexNet in SiamFC; Siam_SHD_BAUS adds a tracking algorithm with a background adaptive update strategy to Siam_SHD; Siam_SHD_BAUS_MAM adds a tracking algorithm with multiple attention mechanisms to Siam_SHD_BAUS; our algorithm is based on Siam_SHD_BAUS_MAM and adds the feature response fusion operation after convolutional inter-correlation. The three ablation experimental algorithms were trained using the GOT-10K dataset and tested on the OTB100 dataset; the test results are shown in Figure 10.  
From Figure 10, it can be seen that using SiamFC as the reference frame, Siam_SHD improves the precision by 1.8% and the success rate by 1.0% on the basis of SiamFC; using Siam_SHD_BAUS as the reference frame, Siam_SHD_BAUS improves the precision by 3.1% and the success rate by 3.3% on the basis of Siam_SHD; using Siam_SHD_BAUS as the reference benchmark, Siam_SHD_BAUS_MAM improves the precision by 0.3% and the success rate by 0.4% on top of Siam_SHD_BAUS; using Siam_SHD_BAUS_MAM as the reference benchmark, our algorithm improves the precision on top of Siam_SHD_BAUS_MAM by 1.7% and success rate improved by 0.7%.The results of the ablation experiments show that