# Foreground Objects Detection by U-Net with Multiple Difference Images
## 4 Experimental Results  
In the experiment, the proposed algorithm is compared with the traditional algorithms of SuBSENSE [2], CwiarD [39], Spectral360 [40], GMM [9], and PAWCS [41] and deep learning-based algorithms of FgSegNet-v2 [42] and modified FgSegNet-v2. The original FgSegNet-v2 [42] algorithm uses one RGB image as the input of a network. We modify it to use multiple difference images as the input of a network, like the proposed algorithm, and we denote it as modified FgSegNet-v2. Data for training consisted of a training set and validation set, and the performance of each algorithm was evaluated using a test set that was not used for training. The following experiment was performed to show the performance of the proposed algorithm. (1) Comparison when using multiple original images and multiple difference images as the input of a network; we show the superiority of the proposed algorithm through this. (2) Comparison between learning using data obtained in one environment and learning using all data obtained in various environments; we show that the proposed algorithm gives improved results in unknown scenes. Experiments were done using the CDnet 2014 dataset [4]. They consisted of 53 scenes from 11 categories, as shown in Table 1, and they dealt with diverse situations that could occur during visual surveillance. We evaluated the foreground object detection algorithms using a variety of metrics that are widely used in visual surveillance, namely, recall, precision, F-measure (FM), percentage of wrong classification (PWC), false positive rate (FPR), false negative rate (FNR), and specificity (SP):  
\[\text{Precision}=\frac{TP}{TP+FP}\]  
\[\text{Recall}=\frac{TP}{TP+FN}\]  
\[\text{FM}=2\times\frac{Precision\times Recall}{Precision+Recall}\]  
\[\text{PWC}=\frac{FP+FN}{TP+TN+FP+FN}\]  
\[\text{FPR}=\frac{FP}{FP+TN}\]  
\[\text{FNR}=\frac{FN}{TP+FN}\]  
\[\text{SP}=\frac{TN}{TN+FP}\]  
where _TP_, _TN_, _FP_, and _FN_ mean true positive, true negative, false positive, and false negative, respectively. \begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**Categories**} & \multicolumn{1}{c}{**Scene Names**} \\ \multicolumn{1}{c}{**(Total Number of Scenes/Number of Scenes Used for Training)**} & \multicolumn{1}{c}{**Highway, Office, Pedestrians, PETS2006**} \\ \hline \hline \multicolumn{1}{c}{Camera Jitter (4/2)} & **Badminton, Sidewalk, Traffic, Boulevard** \\ \hline \hline \multicolumn{1}{c}{Bad Weather (4/2)} & **Skating, Wet snow, Blizzard, Snowfall** \\ \hline \hline \multicolumn{1}{c}{Dynamic Background (6/3)} & **Boats, Canoe, Fountain1, Fountain2, Fall, Overpass** \\ \hline \hline \multicolumn{1}{c}{Intermittent Object Motion (6/2)} & **Abandoned box, Street light, Parking, Sofa, Tram stop, Winter drive way** \\ \hline \hline \multicolumn{1}{c}{Low Framerate (4/2)} & **Port\_0\_17 fps, Tram crossroad\_1 fps, Tunnel exit\_0\_35 fps, Tumpike\_0\_5 fps** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: List of scenes in the CDnet 2014 dataset (bold indicates the scene used in training). In Table 1, bold letters represent the 24 scenes used in the training. We used 200 images for each scene in training. Test statistics are obtained by using scenes not used in the training in Table 1. Ten difference images under a five-frame interval were used as the input of networks. Subtracting by mean was used for the preprocessing of the input data. In both cases, four scenes of the pan-tilt-zoom (PTZ) category were not used in the training. The proposed method has a weakness for the category of PTZ where images are obtained through panning of the camera. In video surveillance, cameras are usually fixed at a predefined location. Scenes in the PTZ category are not common situations in video surveillance. Therefore, experiments are done using 49 scenes from 10 categories, excluding the PTZ category. Computation was done using one Intel i7-7820X CPU and an NVIDIA RTX 2080 Ti GPU. The computation time for each input was 30 ms, which was obtained by averaging the processing time of 100 trials.