## 1 Introduction
A single-object tracking algorithm based on Siamese fully convolutional networks uses an offline dataset to train a similarity matching model, thus transforming the object tracking problem into a similarity matching problem. Although the single-object tracking algorithm based on Siamese networks has achieved good tracking results, it also has high precision and speed and can be applied to some specific scenes. However, since the model uses the object features provided in the first frame of the image as the baseline template throughout the tracking process, the baseline template is not updated, causing the model to be unable to obtain more information conducive to feature discrimination, which leads to the model's inability to adapt to the various changes of the target in the complex environment. The backbone feature extraction network of the model uses AlexNet [27], which can cause the model to fail to extract the deep semantic features of the object because of the small number of layers  
Figure 1: Effect diagram of visual single-object tracking. in the feature extraction layer of AlexNet. Therefore, the algorithm suffers from tracking performance degradation and tracking failure when facing various challenges in complex scenes. To solve the problems of tracking performance degradation and tracking failure of the Siamese full convolutional network-based single-object tracking algorithm in complex scenes for the above reasons, and at the same time, in order to further improve the tracking precision and precision of the algorithm so that the performance of the algorithm meets the requirements of practical applications, we propose a single-object tracking algorithm for Siamese networks that integrates a multiple-attention mechanism and an adaptive updating strategy of the background features. The structure of the algorithm is illustrated in Figure 2.  
The framework of our tracking algorithm is shown in Figure 2. It contains a goal branch and a search branch. The target branch consists of a main branch and a background adaptive update branch; each of them contains a small convolutional and hopping layer connected feature fusion network and multiple attention modules. However, the search branch only uses a small convolutional kernel network and multiple attention modules. Finally, the target branch and the search branch output a response map through a convolutional inter-correlation operation. In this work, we propose a new tracking algorithm based on the Siamese network framework. The main contributions are as follows:  
1. We designed a backbone feature extraction network with a small convolutional kernel and hopping layer connection feature fusion. It can not only effectively extract the deep semantic features of the object, but also fuse the mid-level features with the deep features through the hopping layer connection, which is conducive to the enhancement of the feature expression ability of the network. Meanwhile, utilizing 3 \(\times\) 3 and 1 \(\times\) 1 small convolution kernels for convolution operation can increase the depth of the network while reducing the network parameters. 2. A background feature adaptive updating strategy was proposed in our algorithm and applied to the processing of the object template. First, after the object template is processed through the background feature adaptive updating strategy, it highlights the object features and weakens the background features so that the object features form a sharp contrast with the background features, so that the algorithm can fully distinguish the object and background features in the object template. Then, the object features obtained after processing through the background feature adaptive updating strategy, and then through the backbone feature extraction network and multiple attention mechanisms, are linearly fused with another object feature that has not been processed through the background feature adaptive updating strategy.