## 2 The Proposed Method  
The outline of the proposed method is illustrated in Figure 1. The input image \(I\) is fed into a pretrained convolution layer to obtain a series of convolution feature images \(F\), and then a few feature images that are closest to the original image are selected to merge a new convolution feature image \(\hat{I}\). Subsequently, background subtraction methods are used to deal with \(\hat{I}\) and obtain the foreground image (\(FG\)). It is well known that CNN has made great achievements in computer vision, image processing, and other fields. [PERSON] et al. (2016) showed that the features learned by the CNN are hierarchical. Basically, general features, such as color, edge, line, and so on, are extracted from the lower layers of the neural network. More advanced specific features are extracted from the deep layers; however, these specific features only present the scene features in the corresponding training set, and they cannot be used in other scenarios. On the contrary, the lower layers are used to extract the underlying common features of other scenes. Our framework was based on the VGG16 network ([PERSON] et al., 2016) architecture trained on ImageNet. The first convolution layer, conv1_1, of the VGG16 is applied to extract the general features of the images. The detailed configuration parameters of the conv1_1 layer used in this study are shown in Table 2. The size of the input image was \(W\times H\times 3\), where \(W\) is the width and \(H\) is the height of the image. The feature images with 64 channels were generated by conv1_1, and therefore the output size was \(W\times H\times 64\). The conv1_1 captured low-level information and retained higher spatial resolution. For example, the input image \(I\) was processed by the convolution layer conv1_1 of the VGG16. Then, the feature image (\(F\)) was obtained. \[F=conv(I) \tag{1}\]  
Conv is the convolution operation with a kernel size of \(3\times 3\) and the stride is 1. F contains 64 convolution feature images, and the resolution of each convolution feature image is the same as the input image. The feature images are shown in Figure 2. Our observations of these convolution feature images show that some features take the form of noise-like images, while other features reflect the \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Layer** & **Input Size** & **Kernel** & **Stride** & **Padding** & **Output Size** \\ \hline conv1\_1 & \(W\times H\times 3\) & \(3\times 3\) & 1 & yes & \(W\times H\times 64\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Parameter configuration of conv1_1 layer. Figure 1: The framework of the proposed method. major objects of the input image, such as a canoe and people. Based on these observations, the main convolution features that best represent the input image are reserved, while other features that have a low correlation with the input image are abandoned. Consequently, some irrelevant interference factors are removed from the input image. The performance of the background subtraction method is further refined by using the reserved convolution features instead of the corresponding input images. To reduce the complexity of the computations, the two convolution feature images which have the strongest correlation with the input image are extracted to merge a new merging image. This new image is fed into the background subtraction method and replaces the input image. To evaluate the correlation strength between the convolution feature image and the input image, the value of the peak signal-to-noise ratio (PSNR) is calculated for the convolution feature image and the input image. The higher is the PSNR value, the stronger is the correlation between the convolution image and the input image. \[PSNR=10\cdot log_{10}\left(\frac{MAX_{I}^{2}}{MSE}\right) \tag{2}\]  
where \(MAX_{I}\) is the maximum pixel value of the input image and the data in this study are in the range of 0-1.