## 1 Introduction  
With the rapid development of computer vision technology, visual object tracking has become a research direction with important applications value in the field of computer vision. Visual object tracking has a wide range of application scenarios that can be applied to human-computer interaction, video surveillance, urban transportation, automatic driving, and other fields, offering considerable convenience in our daily lives [1, 2, 3, 4]. The number of tracking objects can be divided into single-object tracking and multi-object tracking. Visual single-object tracking is studied, and its process is as follows: First, the tracking frame of the object to be tracked is given in the first frame of the image of the video sequence, and then the position and size of the tracked object are automatically given in the subsequent frames of the video sequence, as shown in Figure 1. After many years of dedicated research and accumulation by many researchers, although single-object tracking algorithms have made some progress, they still face many challenges in complex scenarios, such as changes in the shape and size of the object, changes in the ambient illumination in which the object is located, similar background interference, and object occlusion [5, 6]. All of these uncontrolled situations can degrade the algorithm's tracking performance, which can lead to tracking drift and failure. Simultaneously, single-object tracking algorithms have high requirements for robustness, precision, precision, and speed in practical applications. Visual single-object tracking algorithms can be categorized into generative, correlation, and deep learning-based object tracking algorithms. The visual single-object tracking algorithm based on the generative model [7; 8; 9; 10] uses the current frame to model the object region and searches for the region that is most similar to the model in the next frame to predict the object's position. Although it can effectively deal with the situation of object loss during the tracking process, it ignores the relationship between the background and the object, which leads to the degradation of the tracking precision in the face of a complex and changeable scene. A visual single-object tracking algorithm based on a correlation filter [11; 12; 13; 14; 15; 16; 17] aims to learn a correlation filter and obtain a dense response map by performing a correlation operation on the image, where the position of the maximum value in the response map is the predicted position of the object. Although it has achieved good results in the field of object tracking, owing to the lack of consideration for the changes in the size of the object's appearance, it is easily disturbed by external environments when confronted with occlusion. However, it has achieved good results in the field of object tracking, owing to the lack of consideration for the change of the object's appearance. When facing complex scenes such as occlusion, deformation, etc., it is easily interfered with by the external environment, which leads to a reduction in tracking precision and success rate. Compared with the above two types of tracking algorithms, the deep learning-based visual single-object tracking algorithm has greatly improved tracking performance and has become the mainstream object tracking algorithm nowadays. Visual single-object tracking may require tracking arbitrary objects in nature; thus, it is not possible to collect data from all objects and train a tracker suitable for all objects in the research of single-object tracking algorithms. With the proposal of Siamese networks [18] and their successful application in computer vision tasks such as face recognition and image matching [19], scholars have proposed Siamese network-based visual single-object tracking algorithms [20; 21; 22; 23; 24; 25; 26] and attracted great attention in the field of object tracking owing to their faster speed and higher tracking performance.