# Background Subtraction for Moving Object Detection in RGBD Data: A Survey
## 6 Comparisons
### Comparisons on the RGB-D Object Detection Dataset
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Video** & **Method** & **PWC** & **FNR** & **FPR** & **Si** & **SI\({}_{\Omega}\)** \\ \hline  & CL\({}_{\text{C}}\) & 2.38 & 0.1638 & 0.0063 & 0.72 & 0.55 \\  & C-KDE & 2.43 & 0.1208 & 0.0088 & 0.81 & 0.66 \\  & CL\({}_{D}\) & 2.06 & 0.0177 & 0.0209 & 0.78 & 0.42 \\  & D-KDE & 0.75 & 0.0098 & 0.0107 & 0.91 & 0.61 \\  & MoG4D & 1.93 & **0.0063** & 0.0209 & 0.79 & 0.45 \\  & ViBeRGB+D & 12.39 & 0.0065 & 0.1385 & 0.44 & 0.12 \\  & MoGRGB+D & 2.03 & 0.1701 & **0.0016** & 0.79 & 0.61 \\ GenSeq & CL\({}_{W}\) & 1.30 & 0.0149 & 0.0127 & 0.83 & 0.53 \\  & AMDF & 0.94 & 0.0956 & 0.0041 & - & - \\  & RFBS & **0.52** & 0.0386 & 0.0045 & **0.92** & - \\  & EC-RGBD & - & - & - & 0.87 & - \\  & MoG-RegPRE & 0.85 & 0.0128 & 0.0079 & 0.88 & - \\  & GSM\({}_{\text{II}}\) & 1.38 & 0.0104 & 0.0144 & 0.83 & **0.78** \\  & GSM\({}_{\text{II}}\) & 1.30 & 0.0408 & 0.0130 & 0.83 & **0.78** \\  & RGBD-KDE & 0.85 & 0.0115 & 0.0058 & 0.87 & 0.65 \\  & SAFBS & 0.60 & 0.0428 & 0.0050 & **0.92** & - \\ \hline  & CL\({}_{\text{C}}\) & 39.02 & 0.8227 & 0.0227 & 0.22 & 0.37 \\  & C-KDE & 16.54 & 0.5972 & 0.0269 & 0.51 & 0.40 \\  & CL\({}_{D}\) & 2.47 & 0.0258 & 0.0238 & 0.91 & **0.78** \\  & D-KDE & 2.68 & 0.0125 & 0.0090 & 0.94 & 0.77 \\  & MoG4D & 3.49 & 0.0038 & 0.0613 & 0.91 & 0.81 \\  & ViBeRGB+D & 6.94 & **0.0017** & 0.1269 & 0.81 & 0.74 \\ ColCamSeq & MoGRGB+D & 38.47 & 0.8287 & **0.0075** & 0.22 & 0.35 \\  & CL\({}_{W}\) & 3.20 & 0.0352 & 0.0292 & 0.89 & 0.77 \\  & AMDF & **1.89** & 0.0387 & 0.0299 & - & - \\  & EC-RGBD & - & - & - & **0.95** & - \\  & GSM\({}_{\text{II}}\) & 2.30 & 0.0710 & 0.0321 & 0.90 & 0.52 \\  & GSM\({}_{\text{II}}\) & 2.20 & 0.0294 & 0.0436 & 0.92 & 0.53 \\  & RGBD-KDE & 2.72 & 0.0299 & 0.0155 & 0.83 & 0.65 \\ \hline  & CL\({}_{\text{C}}\) & 1.78 & 0.1560 & 0.0095 & 0.67 & 0.62 \\  & C-KDE & 2.27 & 0.0689 & 0.0074 & 0.74 & 0.59 \\  & CL\({}_{D}\) & 3.38 & 0.4849 & 0.0064 & 0.40 & 0.39 \\  & D-KDE & 3.47 & 0.3012 & 0.0155 & 0.52 & 0.54 \\  & MoG4D & 2.11 & 0.1525 & 0.0131 & 0.61 & **0.61** \\  & ViBeRGB+D & 9.31 & **0.0548** & 0.0955 & 0.30 & 0.60 \\ DCamSeq & MoGRGB+D & 3.57 & 0.6087 & **0.0009** & 0.32 & 0.27 \\  & CL\({}_{W}\) & 2.46 & 0.3221 & 0.0066 & 0.55 & 0.51 \\  & AMDF & 10.78 & 0.7686 & 0.0684 & - & - \\  & GSM\({}_{\text{II}}\) & 1.74 & 0.2045 & 0.0046 & 0.64 & 0.54 \\  & GSM\({}_{\text{II}}\) & 1.65 & 0.2206 & 0.0061 & 0.65 & 0.55 \\  & RGBD-KDE & **1.62** & 0.1000 & 0.0049 & **0.76** & **0.61** \\ \hline  & CL\({}_{\text{C}}\) & 5.37 & 0.1820 & 0.0323 & 0.67 & 0.63 \\  & C-KDE & 3.90 & 0.0758 & 0.0259 & 0.74 & 0.58 \\  & CL\({}_{D}\) & 0.98 & 0.0095 & 0.0098 & 0.93 & 0.67 \\  & D-KDE & 0.57 & 0.0044 & **0.0041** & 0.88 & 0.70 \\  & MoG4D & 3.94 & 0.0059 & 0.0450 & 0.77 & 0.66 \\  & ViBeRGB+D & 7.15 & **0.0001** & 0.0834 & 0.66 & 0.54 \\  & MoGRGB+D & 3.43 & 0.2351 & 0.0008 & 0.75 & 0.58 \\ ShSeq & CL\({}_{W}\) & 0.81 & 0.0160 & 0.0068 & 0.94 & 0.71 \\  & AMDF & 1.46 & 0.0872 & 0.0047 & - & - \\  & RFBS & 0.47 & 0.0072 & 0.0045 & **0.96** & - \\  & EC-RGBD & - & - & - & 0.91 & - \\  & GSM\({}_{\text{II}}\) & 0.87 & 0.0098 & 0.0088 & 0.93 & 0.76 \\  & GSM\({}_{\text{II}}\) & 1.66 & 0.0014 & 0.0192 & 0.89 & 0.65 \\  & RGBD-KDE & **0.52** & 0.0122 & 0.0049 & 0.93 & **0.80** \\  & SAFBS & 0.65 & 0.0037 & 0.0070 & 0.95 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance results of various background subtraction methods on the RGBD videos of the RGB-D Object Detection dataset. In boldface, the best results for each metric and each sequence.