# Background Subtraction for Moving Object Detection in RGBD Data: A Survey
## 6 Comparisons
### Comparisons on the GSM Dataset  
Performance comparisons on the GSM dataset are reported in Table 7. Here, values for the GSM\({}_{UB}\) and GSM\({}_{UF}\) algorithms by [PERSON] et al. [32] are those reported on the dataset website. Values for the RGBD-KDE algorithm by [PERSON] et al. [80] and the KDE algorithm using only color (C-KDE) and only depth (D-KDE) are those reported in [80]. It can be observed that the compared methods based on the combination of color and depth information robustly deal with all the issues related to RGBD data: intermittent object \begin{table}
\begin{tabular}{l l c c c c c c c} \hline \hline
**Video** & **Method** & **Rec** & **Sp** & **FPR** & **FNR** & **PWC** & F\({}_{1}\) & **Prec** \\ \hline \multirow{6}{*}{Sleeping-ds} & C-KDE & 0.720 & 0.780 & - & - & - & 0.705 & 0.690 \\  & D-KDE & 0.790 & 0.830 & - & - & - & 0.795 & 0.800 \\  & GSM\({}_{UB}\) & 0.810 & **0.980** & **0.020** & 0.190 & 10.390 & 0.890 & **0.980** \\  & GSM\({}_{UF}\) & **0.960** & 0.960 & 0.040 & **0.040** & **3.980** & **0.960** & 0.950 \\  & RGBD-KDE & 0.890 & 0.880 & - & - & - & 0.900 & 0.910 \\ \hline \multirow{6}{*}{TimeOfDay-ds} & C-KDE & 0.150 & 0.480 & - & - & - & 0.233 & 0.520 \\  & D-KDE & 0.370 & 0.610 & - & - & - & 0.469 & 0.640 \\  & GSM\({}_{UB}\) & 0.000 & **1.000** & **0.000** & **0.000** & **0.190** & 0.000 & 0.000 \\  & RGBD-KDE & **0.490** & 0.750 & - & - & - & **0.570** & **0.680** \\ \hline \multirow{6}{*}{Cespatx-ds} & C-KDE & 0.750 & 0.750 & - & - & - & 0.755 & 0.760 \\  & D-KDE & 0.910 & 0.900 & - & - & - & 0.925 & 0.940 \\  & GSM\({}_{UB}\) & 0.960 & **0.990** & **0.010** & 0.040 & 2.890 & 0.970 & **0.990** \\  & GSM\({}_{UF}\) & **0.980** & **0.990** & **0.010** & **0.020** & **1.490** & **0.990** & **0.990** \\  & RGBD-KDE & 0.910 & 0.940 & - & - & - & 0.939 & 0.970 \\ \hline \multirow{6}{*}{Despatx-ds} & C-KDE & 0.880 & 0.910 & - & - & - & 0.909 & 0.940 \\  & D-KDE & 0.720 & 0.700 & - & - & - & 0.739 & 0.760 \\  & GSM\({}_{UB}\) & 0.940 & 0.990 & 0.010 & 0.060 & 3.390 & 0.970 & **0.990** \\  & GSM\({}_{UF}\) & **0.970** & **1.000** & **0.000** & **0.030** & **0.000** & **0.980** & **0.990** \\  & RGBD-KDE & 0.910 & 0.940 & - & - & - & 0.920 & 0.930 \\ \hline \multirow{6}{*}{Shadows-ds} & C-KDE & 0.920 & 0.920 & - & - & - & 0.935 & 0.950 \\  & D-KDE & 0.980 & 0.990 & - & - & - & 0.985 & 0.990 \\  & GSM\({}_{UB}\) & 0.960 & **1.000** & **0.000** & 0.040 & 1.810 & 0.980 & **1.000** \\  & GSM\({}_{UF}\) & 0.980 & **1.000** & **0.000** & **0.020** & **1.040** & 0.990 & 0.990 \\  & RGBD-KDE & **1.000** & 0.990 & - & - & - & **1.000** & **1.000** \\ \hline \multirow{6}{*}{LightSwitch-ds} & GSM\({}_{UB}\) & **0.000** & **1.000** & **0.000** & **0.000** & **0.110** & **0.000** & **0.000** \\  & GSM\({}_{UF}\) & **0.000** & **1.000** & **0.000** & **0.000** & 0.340 & **0.000** & **0.000** \\ \hline \multirow{6}{*}{Bootstraping-ds} & C-KDE & 0.840 & 0.880 & - & - & - & 0.860 & 0.880 \\  & D-KDE & 0.870 & 0.940 & - & - & - & 0.908 & 0.950 \\  & GSM\({}_{UB}\) & 0.740 & **1.000** & **0.000** & 0.260 & 6.940 & 0.850 & 0.980 \\  & GSM\({}_{UF}\) & 0.850 & 0.990 & 0.010 & **0.150** & **3.910** & 0.910 & 0.980 \\  & RGBD-KDE & **0.910** & 0.980 & - & - & - & **0.953** & **1.000** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance results of various background subtraction methods on the RGBD videos of the GSM dataset. In boldface, the best results for each metric and each sequence. Figure 4: GenSeq video from the RGB-D Object Detection dataset. (**a**) RGB image; (**b**) Depth image; (**c**) GT. motion (Sleeping-ds), illumination changes (TimeOfDay-ds and LightSwitch-ds), color camouflage (Cespatx-ds), depth camouflage (Despatx-ds, see Figure 5), color and depth shadows (Shadows-ds), and bootstrapping (Bootstrapping-ds). It should be pointed out that, in the case of TimeOfDay-ds and Ls-ds sequences, the performance analysis should be based on Specificity, FPR, FNR, and PWC, rather than on the other three metrics.