## 2 Research Work
### Adaptive Updating Strategy for Background Features  
According to the working principle of human visual system, there always exists differentiation between the object and the surrounding background, and the greater the differentiation between them, the more the human visual system can distinguish the object and the background, and vice versa, the smaller the differentiation between them, the more the human visual system can't distinguish the object and the background, according to this principle, we propose an adaptive updating strategy of the background features to be integrated into the Siamese network single-object tracking algorithm. First, preprocessing is performed in the first frame image, and the object template image is produced by cropping and scaling, containing the object region image and the surrounding background region image, and the object template image is divided into two branches: (1) One branch enters into the backbone feature extraction network for feature extraction, and after the completion of the feature extraction, it is divided into two branches; one branch enters into the network that incorporates a variety of attention mechanisms, and the other branch maintains the features after the backbone feature extraction network. (2) The other branch enters the background adaptive updating strategy for background feature adaptive updating. The flow of the background adaptive updating strategy is shown below:  
In the first step, the object template image is cropped in the first frame image by  
\[z=\sqrt{(w+2p)\times(h+2p)}, \tag{2}\]  
and then the object template image is scaled by  
\[Sz\times Sz=A^{2}, \tag{3}\]  
where \(p=(w+h)/4\) is the context of our target image patch, \(z\) denotes the size of the cropped object template image, w and h denote the width and height of the object in the original image, respectively, and \(S\) denotes a type of transformation of the image, where the scaling transformation is used, \(A=127\), which means that the cropped object template image is scaled to \(127\times 127\). In the second step, the width and height of the object region image in the scaled object template image were calculated as  
\[z_{h}=\frac{A\times h}{z},\,z_{w}=\frac{A\times w}{z}, \tag{4}\]  
and then the left, top, right, and bottom coordinates of the object region image in the scaled object template image were calculated as  
\[ \begin{array}{l}l=\frac{A-z_{w}}{2},\,t=\frac{A-z_{h}}{2}\\ r=\frac{A+z_{w}}{2},\,b=\frac{A+z_{h}}{2}\end{array} , \tag{5}\]  
where \(z_{h},z_{w}\) denote the width and height of the object region image in the scaled object template image, and \(l\), \(t\), \(r\), and \(b\) denote the left, top, right, and bottom coordinates of the object region image in the scaled object template image. In the third step, the object region image is cropped into the object template image, and the pixel mean value of the object region image is calculated as  
\[P_{om}=\frac{1}{3}\sum_{i=0}^{i=2}{(\frac{1}{m\times n}\sum_{m,n=0,0}^{m,n=x_ {w},z_{h}}P_{mn}^{i})}, \tag{6}\]where \(m\),\(n\) denotes the width and height of the object region image, \(P_{mn}\) denotes the pixel value corresponding to (\(m\),\(n\)) coordinates, and \(i\) denotes the color channel number. In the fourth step, the size of the surrounding background region image was calculated in the object template image using Equation (7), and the pixel value of the object region image was set to 0. The mean pixel value of the surrounding background region image was calculated using Equation (8). \[b_{s}=A^{2}-(r-l)\times(b-t), \tag{7}\]  
\[P_{bm}=\frac{1}{3}\sum_{i=0}^{i=2}(\frac{1}{b_{s}}\sum_{m,n=0,0}^{m,n=A,A}P_{ mm}^{i}), \tag{8}\]  
where \(b_{s}\) denotes the size of the surrounding background region image, \(m\),\(n\) denotes the width and height of the object template image, \(P_{mn}\) denotes the pixel value corresponding to the (\(m\),\(n\)) coordinate, and \(i\) denotes the color channel number.