## 2 Research Work
### Adaptive Updating Strategy for Background Features
In the fifth step, the larger the difference between the average value of the target pixels and the average value of the background pixels, the higher the differentiation between the target and the background. Conversely, the smaller the difference between the two, the lower the differentiation between the target and the background. Therefore, we define a contrast ratio to represent the difference between the target and the surrounding background, as shown in Equation (9); the larger the contrast, the easier it is to distinguish the target. Conversely, the smaller the contrast, the less easy it is to distinguish the target. \[contrast=\frac{|P_{\text{om}}-P_{bm}|}{P_{om}+P_{bm}}, \tag{9}\]  
In the sixth step, the background is adaptively updated: if \(P_{om}>127\) and \(contrast<0.5\), it means that the object area image is not significantly differentiated from the surrounding background area image; then, the pixel value of the surrounding background area in the object template image is set to 0, in order to enhance the model's ability to differentiate between the object area and the background area; if \(P_{om}<127\) and \(contrast<0.5\), it means that the object area image is not significantly differentiated from the image of the surrounding background region, then the pixel value of the surrounding background region in the object template image is set to 255 to enhance the model's ability to differentiate between the object region and the background region; in all other cases, it means that the object region image is significantly differentiated from the image of the surrounding background region, and there is no need to perform any processing for the surrounding background region as shown in Equation (10). \[background=\left\{ \begin{array}{ll}0,&if\ P_{om}>127\ and\ contrast<0.5\\ 255,&if\ P_{om}<127\ and\ contrast<0.5\\ \text{preserve,}&Other\\ \end{array} \right., \tag{10}\]  
After the above six steps, the background adaptive update is completed, and after the update is completed, it enters the main feature extraction network for feature extraction, and after the completion of feature extraction, it enters the network that fuses multiple attention mechanisms, and then linearly fuses the branch features with another branch feature that carries out main feature extraction and fuses multiple attention mechanisms, and the linear fusion is as shown in Equation (11). This not only makes full use of the information of the object template image in the first frame image but also enhances the model's ability to discriminate between the object and background. \[f_{M}=\lambda_{1}\times\delta(f)+\lambda_{2}\times\delta(f_{b}), \tag{11}\]  
where \(f_{M}\in R^{W\times H\times C}\), \(\delta\) denotes the fusion of multiple attentional mechanisms, \(f\) denotes the object feature that has not been processed by the background adaptive updating strategy,\(f\in R^{W\times H\times C}\), \(\lambda_{1}\) denotes the proportion coefficient of the object feature that has not been processed by the background adaptive updating strategy, \(f_{b}\) denotes the object feature that has been processed by the background adaptive updating strategy, \(f_{b}\in R^{W\times H\times C}\), \(\lambda_{2}\) denotes the proportion coefficient of the object feature that has been processed by the background adaptive updating strategy.