# Foreground Objects Detection by U-Net with Multiple Difference Images
## 4 Experimental Results
### Generalization Ability Test Using Scenes Not Used in Training  
Having a good generalization power is one of the main goals of machine learning. Though deep learning has shown a big jump in performance in various areas, it still requires an improvement in the generalization power. We show the improved generalization power of the proposed method by applying it on the scenes that are not used in the training. The proposed algorithm is compared to three algorithms, SuBSENSE [2], modified FgSegNet-v2, and FgSegNet-v2 [42]. We adjust FgSegNet-v2 to use multiple difference images as input, like the proposed method, and we denote it as modified FgSegNet-v2. Experiments were done by training the proposed method, modified FgSegNet-v2, and FgSegNet-v2 using the same 24 scenes in the CDnet 2014 dataset, which are shown in Table 1.  
First, we evaluate the generalization power on the CDnet 2014 dataset. We investigated the generalization ability by applying the trained networks to the other 29 scenes that were not used in training in the CDnet 2014 dataset. Second, we present the results obtained by applying the trained networks to scenes in the SBI2015 dataset [43] and scenes that we acquired ourselves. The SBI2015 dataset and scenes that we acquired were not used in training. Figure 4 shows the results obtained on scenes used for training in the CDnet 2014 dataset. Figure 4a shows the original image and the corresponding frame number of the scene. Figure 4b shows the ground truth segmentation map. The results of SuBSENSE [2], the proposed method, modified FgSegNet-v2, and FgSegNet-v2 [42] are presented in Figure 4b-e. The deep learning-based methods of the proposed method, modified FgSegNet-v2, and FgSegNet-v2 give better results than the traditional approach of SuBSENSE [2]. Through this, we can ascertain that deep learning-based algorithms give superior results compared to traditional a BGS algorithm in scenes used in the training. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Number of Difference Images**} & \multicolumn{2}{c}{**All Scenes**} & \multicolumn{2}{c}{**Scenes Used for Training**} & \multicolumn{2}{c}{**Scenes Not Used for Training**} \\ \cline{2-7}  & **FM** & **PWC** & **FM** & **PWC** & **FM** & **PWC** \\ \hline \(5\) & \(0.84\) & \(1.10\) & \(0.95\) & \(0.30\) & \(0.74\) & \(1.86\) \\ \(10\) & \(0.86\) & \(1.03\) & \(0.96\) & \(0.16\) & \(0.77\) & \(1.87\) \\ \(15\) & \(0.85\) & \(1.06\) & \(0.96\) & \(0.26\) & \(0.75\) & \(1.84\) \\ \(20\) & \(0.84\) & \(1.09\) & \(0.93\) & \(0.30\) & \(0.75\) & \(1.86\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: A comparison result by changing the number of difference images under a five-frame interval. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Number of Difference Images**} & \multicolumn{2}{c}{**All Scenes**} & \multicolumn{2}{c}{**Scenes Used for Training**} & \multicolumn{2}{c}{**Scenes Not Used for Training**} \\ \cline{2-7}  & **FM** & **PWC** & **FM** & **PWC** & **FM** & **PWC** \\ \hline \(2\) & \(0.85\) & \(1.24\) & \(0.96\) & \(0.18\) & \(0.75\) & \(2.26\) \\ \(5\) & \(0.86\) & \(1.06\) & \(0.97\) & \(0.13\) & \(0.76\) & \(1.95\) \\ \(10\) & \(0.86\) & \(1.03\) & \(0.96\) & \(0.16\) & \(0.77\) & \(1.87\) \\ \(50\) & \(0.84\) & \(1.22\) & \(0.96\) & \(0.18\) & \(0.72\) & \(2.21\) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: A comparison result by changing frame intervals to within 50 frames. Figure 5 shows the results obtained on scenes that were not used for training in the CDnet 2014 dataset. We can notice a clearly different tendency in Figure 4. FgSegNet-v2 [42] produces the worst results among the four algorithms. It produces even worse results than the non-deep learning-based method of SuBSENSE [2]. We can conclude that the original FgSegNet-v2 is efficient in scenes that were used for training, and it has little generalization ability.