# Foreground Objects Detection by U-Net with Multiple Difference Images
## 4 Experimental Results
In the case of FgSegNet-v2 [42], a deep learning-based method, it took 9 ms to process one image in the same PC environment. This is a faster processing speed than the proposed method, but the proposed method shows much better generalization ability in the real environment. Additionally, the proposed method can be computed over 30 fps. Therefore, it was judged that the proposed method has an appropriate level of model size and computational cost to use in a real environment. First, we present the experimental results of training using only one scene in the CDnet 2014 dataset. After training using one scene, we applied it to other scenes to assess the generalization ability. Table 2 shows a comparison of the proposed method and FgSegNet-v2 [42], which produces state-of-the-art results on the CDnet 2014 dataset. FgSegNet-v2 was used to train a separate network for each scene in the CDnet 2014 dataset, and test statistics were obtained for each scene. The proposed method and FgSegNet-v2 were trained using only a highway scene in the CDnet 2014 dataset. FgSegNet-v2 uses one RGB image as input, while the proposed algorithm uses 10 difference images as input. FgSegNet-v2 achieved a better result than the proposed algorithm in a scene that was used in the training. Though the proposed method showed dramatic improvement in comparison to FgSegNet-v2 for other scenes that were not used in the training, the overall performance of the proposed method still requires improvement because it achieves much lower performance than SuBSENSE [2]. We can conclude that training using only a highway scene does not guarantee generalization power for other scenes. Therefore, we trained the proposed algorithm using all the scenes except the PTZ category in the CDnet 2014 dataset to improve the generalization ability. \begin{table}
\begin{tabular}{c c} \hline
**Categories** & **Scene Names** \\ \hline Night Videos (6/3) & **Bridge entry, busy boulevard, fluid highway**, Street corner \\  & at night, Tram station, Winter street \\ \hline PTZ (4/0) & Continuous pan, Intermittent pan, Two position ptz cam, Zoom in zoom out \\ \hline Shadow (6/3) & **Back door, Copy machine, Bungalows**, Bus station, Cubic, People in shade \\ \hline Thermal (5/3) & **Corridor, Library, Lakeside**, Dining room, Park \\ \hline Turbulence (4/2) & **Turbulence0, Turbulence1**, Turbulence2, Turbulence3 \\ \hline \end{tabular}
\end{table} 
Table 1: _Cont._Table 3 and 4 show the result of the proposed algorithm, which was trained using 24 scenes in CDnet 2014 dataset. The proposed method shows superior performance compared to other algorithms, with FM scores of 0.927 and 0.895, respectively, even in \"Bad Weather\" and \"Dynamic Background\" categories, where a large amount of noise is included in the difference image. Table 4 shows a comparison of the results obtained by the proposed algorithm and other algorithms. The proposed method, FgSegNet-v2 [42], and modified FgSegNet-v2 were trained using the same 24 scenes in the CDnet 2014 dataset. The proposed method, FgSegNet-v2, and modified FgSegNet-v2 are deep learning-based algorithms that require training samples. SuBSENSE [2], CwisarD [39], Spectral-360 [40], and GMM [9] are traditional algorithms that do not require training samples, and their experimental