## 2 The Proposed Method
Figure 4: Refinements of the background-subtraction methods on dynamic background. To verify the ability of the proposed method, Neural Response Mixture (NeRM) [39] was selected for comparison. NeRM takes advantage of rich deep features extracted from the neural responses of an efficient, stochastically-formed deep neural network for constructing Gaussian mixture models to detect motion in a scene. Table 5 shows the improvement ratio of GMM by NeRM and the proposed method respectively, in which the higher the ratio, the better the performance. The most representative F-measure evaluation metric of the proposed method is better. For further evaluating the performance of the proposed method, shadow videos are used to test. Shadow is composed of six videos with both strong and soft moving and cast shadows [20]. Table 6 shows the details of this category. The refinement results are illustrated in Figure 6. Table 7 lists the quantitative comparison results. As shown in Table 7, GMM, KDE, PBAS, LOBSTER and ViBe methods were refined in a certain degree. Since SuBSENSE had high performance, this method was not improved. Since the PSNR is simple and easy to realize, it was adopted to determine the feature images involved in the proposed method in our experiments. Structural similarity index measure (SSIM) is another well-known objective image quality metric. A simple analytical link exists between the PSNR and the SSIM and the PSNR is more sensitive to Gaussian noise than the SSIM [40]. The SSIM used to choose the convolution feature images was evaluated in the experiment as PSNR. Table 8 shows the comparison results. As can been observed, the PSNR metric was better than the SSIM. The input images of dynamic scenes usually contain a large amount of redundant information that results in deterioration of background modeling performance while the original images of a video are used to model the background. The convolution of CNN, which was pretrained on ImageNet, extracted the underlying features of the input image. The dynamic redundancy features were abandoned, and the main features were merged into a new input image. On the basis of the new image, the background model, which was built by the \(BGS\) method, had better performance. In the proposed framework, only one lower layer convolution of CNN was used. Compared with a whole CNN network, which is often hundreds of megabytes in size, one convolution layer is only several megabytes. Therefore, the performance of the traditional background subtraction method is further improved without \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Category** & **Methods** & **Recall** & **FPR** & **FNR** & **PBC** & **Precision** & **F-Measure** \\ \hline GMM & 0.7020 & 0.0124 & 0.2980 & 2.6666 & 0.7128 & 0.6862 \\ GMM\({}_{\text{con}}\) & 0.7190 & 0.0127 & 0.2810 & 2.7459 & 0.7497 & 0.7240 \\ SuBENSE & 0.9469 & 0.0081 & 0.0531 & 0.9960 & 0.8627 & 0.8998 \\ SuBENSE\({}_{\text{con}}\) & 0.9091 & 0.0085 & 0.0909 & 1.1977 & 0.8661 & 0.8850 \\ PBAS & 0.6917 & 0.0076 & 0.3083 & 2.1497 & 0.8487 & 0.7455 \\ PBAS\({}_{\text{con}}\) & 0.7189 & 0.0079 & 0.2811 & 2.2203 & 0.8655 & 0.7729 \\ KDE & 0.9269 & 0.0757 & 0.0731 & 7.6690 & 0.3913 & 0.5176 \\ KDE\({}_{\text{con}}\) & 0.6899 & 0.0122 & 0.3101 & 2.5481 & 0.7901 & 0.7275 \\ LOBSTER & 0.8038 & 0.0063 & 0.1962 & 1.4937 & 0.9008 & 0.8452 \\ LOBSTER\({}_{\text{con}}\) & 0.8773 & 0.0070 & 0.1227 & 1.1703 & 0.8739 & 0.8709 \\ ViBe & 0.6600 & 0.0064 & 0.3400 & 2.0278 & 0.8622 & 0.7397 \\ ViBe\({}_{\text{con}}\) & 0.6944 & 0.0062 & 0.3056 & 1.9924 & 0.8877 & 0.7746 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparisons between \(BGS\) and \(BGS_{\text{con}}\) by the proposed method. Figure 6: Refinements of the background-subtraction methods on shadow videos. increasing computational load.