# Foreground Objects Detection by U-Net with Multiple Difference Images
## 4 Experimental Results
### Generalization Ability Test Using Scenes Not Used in Training
This can also be noticed quantitatively in Table 4. The proposed method and modified FgSegNet-v2 achieve better results than SuBSENSE even in scenes not used in training. Through this, it can be confirmed that the generalization ability is improved considerably by simply changing the input structure without changing the structure of the deep learning model. Figure 4: Test results on scenes used for the training in the CDnet 2014 dataset: (**a**) ground truth foreground maps, (**b**) proposed method, (**c**) SuBSENSE [2], (**d**) modified FgSegNet-v2, (**e**) FgSegNet-v2 [42]. We present quantitative results obtained using the SBI2015 dataset [43] to show the generalization ability of proposed method. SBI2015 provides 14 scenes in total. We do not use the Toscana scene because it consists of six images that are not continuous. In addition, \"Snellen\" and \"Foliage\" scenes treated moving leaves as foreground labels. This classification differs from the foreground concept used in video surveillance. Moving leaves are generally classified as dynamic background, and we think that they should be treated as background labels. Therefore, in experiments, \"Snellen\" and \"Foliage\" scenes were also excluded from the evaluation. The proposed method, FgSegNet-v2 [42], PAWCS [41], and the SuBSENSE [2] algorithm were compared, and the results are shown in Table 8. The proposed method achieved a better performance than other algorithms. The proposed method shows low FM scores in the \"Candela\" and \"People&coliage\" scenes. Since the proposed method receives images in a range of 50 frames, it shows insufficient performance in the \"Candela\" scene where there is a foreground object that has been stopped for a long time. Additionally, in the \"People&Foliage\" scene, both moving people and bushes are classified as foreground objects. In visual surveillance, moving bushes should be classified as dynamic background, but in the scene they are classified as foreground, so most methods, including the proposed method, show very low performance. Furthermore, FgSegNet-v2  
Figure 5: Test results obtained on scenes not used for training in the CDnet 2014 dataset: (**a**) ground truth foreground maps, (**b**) proposed method, (**c**) SuBSENSE [2], (**d**) modified FgSegNet-v2, (**e**) FgSegNet-v2 [42]. achieved much lower performance than the PAWCS and SuBSENSE algorithms, as seen in Figure 5.  
Figure 6 shows some representative results obtained using images in the SBI2015 dataset. We can notice that the proposed algorithm shows more improvement than traditional background model-based algorithms in the SBI dataset compared to the CDnet 2014 dataset. We think that this is caused by the differences in those datasets. The CDnet 2014 dataset provides a preparation section to generate background model images before a test, but the SBI dataset does not provide this. Therefore, background model-based algorithms have difficulties in the generation of good background model images in the first part of the SBI dataset. Figure 7 shows the results obtained by using scenes that we acquired ourselves. We only show qualitative results because obtaining a ground truth segmentation map with pixel-wise resolution would requires a huge amount of time. Two sets of results obtained using the proposed method are presented in Figure 7. One was trained using 24 scenes in the CDnet 2014 dataset. The other was trained using 49 scenes in the CDnet 2014 dataset. In Figure 7, the SeoulTech #175 image was acquired with a small jitter of the camera and there are no foreground objects in the scene. Overall, the proposed method trained using 49 scenes achieved better results than when it was trained using 24 scenes. Through this, we can see that if additional datasets can be obtained, better performance can be expected. We can conclude that the proposed method can stably detect foreground objects even in new environments that are not shown in the CDnet dataset.