# Foreground Objects Detection by U-Net with Multiple Difference Images
## 4 Experimental Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Scenes** & **Proposed** & \begin{tabular}{c} **Modified** \\ **FgSegNet-v2** \\ \end{tabular} & \begin{tabular}{c} **FgSegNet-v2** \\ \end{tabular} & \begin{tabular}{c} **SuBSENSE** \\ \end{tabular} & \
\begin{tabular}{c} **CwisarD** \\ \end{tabular} &
\begin{tabular}{c} **Spectral-360** \\ \end{tabular} & **GMM [9]** \\ \hline Baseline & **0.954** & 0.940 & 0.814 & 0.950 & 0.908 & 0.933 & 0.825 \\ Camera Jitter & 0.776 & 0.769 & 0.613 & **0.815** & 0.781 & 0.716 & 0.597 \\ Bad Weather & 0.927 & 0.919 & 0.876 & 0.862 & 0.684 & 0.757 & 0.738 \\ Dynamic & **0.895** & 0.883 & 0.619 & 0.818 & 0.809 & 0.787 & 0.633 \\ Background & **0.751** & 0.719 & 0.584 & 0.657 & 0.567 & 0.566 & 0.633 \\ Low Framerate & **0.785** & 0.750 & 0.742 & 0.645 & 0.641 & 0.644 & 0.537 \\ Night Videos & **0.855** & 0.831 & 0.703 & 0.560 & 0.374 & 0.483 & 0.410 \\ Shadow & **0.911** & 0.893 & 0.734 & 0.899 & 0.841 & 0.884 & 0.737 \\ Thermal & **0.932** & 0.929 & 0.799 & 0.817 & 0.762 & 0.776 & 0.662 \\ Turbulence & 0.854 & **0.896** & 0.521 & 0.779 & 0.723 & 0.543 & 0.466 \\ Average & **0.864** & 0.850 & 0.697 & 0.777 & 0.706 & 0.706 & 0.619 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison result of FM score by proposed method and other methods on the CDnet 2014 dataset. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  &
\begin{tabular}{c} **Highway** \\ **Scene** \\ \end{tabular} & \multicolumn{2}{c}{**All Scenes**} & \multicolumn{2}{c}{**Scenes Not Used in Training**} \\ \cline{2-6}  & **FM** & **PWC** & **FM** & **PWC** & **FM** & **PWC** \\ \hline Proposed & 0.99 & 0.08 & 0.47 & 3.61 & 0.46 & 3.68 \\ \hline FgSegNet-v2 [42] & 1.00 & 0.02 & 0.25 & 11.1 & 0.23 & 11.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of results obtained by training using one scene (Highway) in the CDnet 2014 dataset. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Categories** & **FM** & **PWC** & **Recall** & **Precision** & **FPR** & **FNR** & **SP** \\ \hline Baseline & 0.9535 & 0.1301 & 0.9481 & 0.9597 & 0.0006 & 0.0519 & 0.9994 \\ Camera Jitter & 0.7759 & 3.5410 & 0.7563 & 0.8461 & 0.0084 & 0.2437 & 0.9916 \\ Bad Weather & 0.9266 & 0.1741 & 0.9628 & 0.9007 & 0.0012 & 0.0372 & 0.9988 \\ Dynamic BG & 0.8952 & 0.2805 & 0.8892 & 0.9033 & 0.0012 & 0.1108 & 0.9988 \\ Int. Obj. Motion & 0.7509 & 3.0798 & 0.9169 & 0.6792 & 0.0306 & 0.0831 & 0.9694 \\ Low Framerate & 0.7854 & 0.9016 & 0.9256 & 0.7204 & 0.0088 & 0.0744 & 0.9912 \\ Night Videos & 0.8553 & 0.5602 & 0.8717 & 0.8437 & 0.0035 & 0.1283 & 0.9965 \\ Shadow & 0.9108 & 0.6420 & 0.9251 & 0.9037 & 0.0042 & 0.0749 & 0.9958 \\ Thermal & 0.9319 & 0.6305 & 0.9688 & 0.9006 & 0.0059 & 0.0312 & 0.9941 \\ Turbulence & 0.8536 & 0.2404 & 0.9766 & 0.7881 & 0.0023 & 0.0235 & 0.9977 \\ Average & 0.8635 & 1.0301 & 0.9130 & 0.8437 & 0.0072 & 0.0870 & 0.9928 \\ Scenes used in training & 0.9649 & 0.1580 & 0.9788 & 0.9529 & 0.0013 & 0.0212 & 0.9987 \\ Scenes not used in training & 0.7662 & 1.8674 & 0.8499 & 0.7389 & 0.0128 & 0.1501 & 0.9872 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of proposed method which is trained using 24 scenes in CDnet 2014 dataset. statistics shown in Table 4 are those reported in the literature. The proposed algorithm achieved the best performance, except for camera jitter and turbulence categories in the CDnet 2014 dataset. Training the original FgSegNet-v2 using 24 scenes in the CDnet 2014 dataset produced an even worse performance than the traditional SuBSENSE algorithm [2]. Simply training using multiple scenes without changing the network cannot guarantee generalization power. The proposed algorithm, which uses multiple difference images as input, achieves a meaningful improvement. We can conclude that the proposed algorithm provides greater generalization ability than other algorithms. The original FgSegNet-v2 has no generalization ability in other scenes that are not used in training.