# Background Subtraction for Moving Object Detection in RGBD Data: A Survey
## 5 Datasets  
Several RGBD datasets exist for different tasks, including object detection and tracking, object and scene recognition, human activity analysis, 3D-simultaneous localization and mapping (SLAM), and hand gesture recognition (e.g., see surveys in [24; 86; 87]). However, depending on the application they have been devised for, they can include single RGBD images instead of videos, or they can supply GTs in the form of bounding boxes, 3D geometries, camera trajectories, 6 DOF poses, or dense multi-class labels, rather than GT foreground masks. In Table 3, we summarize some publicly available RGBD datasets suitable for background subtraction that include videos and, eventually, GT foreground masks. Specifically, we report their acronym, website, and reference publication (column Name & Refs. ), the source for depth data (column Source), whether or not they also provide GT foreground masks (column GT masks), the number of videos they include (column No. of videos), some RGBD background subtraction methods adopting them for their evaluation (column Adopted by), and the main application they have been devised for (column Main application). The GSM dataset [32] includes seven different sequences designed to test some of the main problems in scene modeling when both color and depth information are used: color camouflage, depth camouflage, color shadows, smooth and sudden illumination changes, and bootstrapping. Each sequence is provided with some hand-labeled GT foreground masks. All the sequences are also included in the SBM-RGBD dataset [33] and accompanied by 56 GT foreground masks. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Name** & **Acronym** & **Computed as** & **Better if** \\ \hline Similarity (or Jaccard index) & Si & TP/(TP + FP + FN) & \(\uparrow\) \\ Similarity in \(\partial\Omega\) & Si\({}_{\partial\Omega}\) & TP/(TP + FP + FN) in \(\partial\Omega\) & \(\uparrow\) \\ Recall & Rec & TP/(TP + FN) & \(\uparrow\) \\ Specificity & Sp & TN/(TN + FP) & \(\uparrow\) \\ False Positive Rate & FPR & FP/(FP + TN) & \(\downarrow\) \\ False Negative Rate & FNR & FN/(TP + FN) & \(\downarrow\) \\ Percentage of Wrong Classifications & PWC & 100 \(\times\) (FP + FN)/(TP + FN + FP + TN) & \(\downarrow\) \\ Precision & Prec & TP/(TP + FP) & \(\uparrow\) \\ F-Measure & F\({}_{1}\) & (2 \(\times\) Prec \(\times\) Rec)/(Prec + Rec) & \(\uparrow\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Metrics frequently adopted for evaluating background subtraction methods in RGBD videos. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
\begin{tabular}{c} Background \\ subtraction \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Some publicly available RGBD datasets for background subtraction. The Kinect dataset [90] contains nine single person sequences, recorded with a Kinect camera, to show depth and color camouflage situations that are prone to errors in color-depth scenarios. The MICA-FALL dataset [71] contains RGBD videos for the analysis of human activities, mainly fall detection. Two scenarios are considered for capturing activities that happen at the center field of view of one of the four Kinect sensors or at the cross-view of two or more sensors. Besides color and depth data, accelerometer information and the coordinates of 20 skeleton joints are provided for every frame. The MULTIVISION dataset consists of two different sets of sequences for the objective evaluation of background subtraction algorithms based on depth information as well as color images. The first set (MULTIVISION Stereo [15]) consists of four sequences recorded by stereo cameras, combined with three different disparity estimation algorithms [103; 104; 105]. The sequences are devised to test color saturation, color and depth camouflage, color shadows, low lighting, flickering lights, and sudden illumination changes.