# AR Long-Term Tracking Combining Multi-Attention and Template Updating
## 1 Introduction  
Augmented Reality (AR) [1] can seamlessly integrate virtual information with the real world to achieve an immersive effect. It has been extensively used in industry, military, gaming, medicine, and other fields, bringing great convenience to production and life. A crucial technology of the AR system is the tracking registration technology, which determines whether the augmented reality system can accurately overlay virtual information into the real scene in real time. Target-tracking technology can realize the tracking and positioning of objects, scenes, people, and other targets, providing more accurate, stable and reliable information support for augmented reality applications. However, challenges such as occlusion, similar target interference, and motion blur often occur in long-term tracking, which can easily lead to tracking failure and result in an incorrect superposition of virtual objects (also known as the drift phenomenon). Therefore, it is of great practical significance for augmented reality technology to improve tracking accuracy and real-time performance of target-tracking technology in complex scenes. In previous work, augmented reality target tracking is usually completed by tracking based on feature points, optical flow method, and correlation filter tracking algorithm. Among them, tracking methods based on feature points, such as SIFT [2], SURF [3], and ORB [4], to name a few, mainly extract robust feature points in images, generate descriptors, and then conduct feature matching to determine the target location. However, tracking only by this feature-matching method is easy to be affected by illumination, changes in the appearance of the target, and so on, leading to tracking failure, and the robustness is not high in practical application. The optical flow method uses the brightness variation between pixels to calculate the motion vector. This method is unstable in complex scenarios and will cause cumulative errors in long-term tracking, leading to tracking failure. Traditional correlation filtering algorithms, such as MOSSE [5], KCF [6], DSST [7], and CSK [8], to name a few, usually adopt manual features, which have poor accuracy and robustness compared with depth features. So, they usually face challenges in maintaining accuracy in complex environments due to factors such as target occlusion, similar target interference, and scale transformation, leading to tracking failures. Due to the continuous development of deep learning technology in recent years, which provides new ideas for target-tracking technology, researchers have worked extensively on target tracking based on deep learning [9, 10]. Among them, tracking-based Siamese networks [11, 12, 13, 14, 15, 16, 17, 18] with both real time and robustness have attracted wide attention. Most trackers based on Siamese networks mostly use shallow convolutional networks (such as AlexNet [19]) for feature extraction. However, as shallow networks cannot extract deep semantic information, their feature expression ability is limited. At the same time, most well-known trackers (such as SINT [20], SiamFC [12], and SiamRPN [13]) usually calculate the similarity between template branches and search branches to achieve the function of feature fusion. However, it is easy to lose a lot of context information by using only this linear matching method, and it is easy to lead to tracking failure when encountering complex environments. In addition, most trackers (SiamDW [21], SiamRPN [13], and SiamFC [12]) do not have the operation of online template updating, which makes it more difficult for trackers to track targets with large appearance changes, occlusion, to name a few. Therefore, we choose the object-tracking method based on Siamese to carry on in-depth research and make some improvements on the basis of it. To solve these problems, we have made some improvements on the basis of the Siamese network.