# Background Subtraction for Moving Object Detection in RGBD Data: A Survey
## 5 Datasets
The second set (MULTIVISION Kinect [58]) consists of four sequences recorded by a Kinect camera, devised to test out of sensor range depth data, color and depth camouflage, flickering lights, and sudden illumination changes. For all the sequences, some frames have been hand-segmented to provide GT foreground masks. The four MULTIVISION Kinect sequences are also included in the SBM-RGBD dataset [33] and accompanied by 294 GT foreground masks. The Princeton Tracking Benchmark dataset [95] includes 100 videos covering many realistic cases, such as deformable objects, moving camera, different occlusion conditions, and a variety of clutter backgrounds. The GTs are manual annotations in the form of bounding-boxes drawn around the objects on each frame. One of the sequences (namely, sequence bear_front) is also included in the SBM-RGBD dataset [33] and accompanied by 15 GT foreground masks. The RGB-D Object Detection dataset [17] includes four different sequences of indoor environments, acquired with a Kinect camera, that contain different demanding situations, such as color and depth camouflage or cast shadows. For each sequence, a hand-labeled ground truth is provided to test foreground/background segmentation algorithms. All the sequences, suitably subdivided and reorganized, are also included in the SBM-RGBD dataset [33] and accompanied by more than 1100 GT foreground masks. The RGB-D People dataset [98] is devoted to evaluating people detection and tracking algorithms for robotics, interactive systems, and intelligent vehicles. It includes more than 3000 RGBD frames acquired in a university hall from three vertically mounted Kinect sensors. The data contains walking and standing persons seen from different orientations and with different levels of occlusions. Regarding the ground truth, all frames are annotated manually to contain bounding boxes in the 2D depth image space and the visibility status of subjects. Unfortunately, the GT foreground masks built and used in [62] are not available. The SBM-RGBD dataset [33] is a publicly available benchmarking framework specifically designed to evaluate and compare scene background modeling methods for moving object detection on RGBD videos. It involves the most extensive RGBD video dataset ever made for this specific purpose and also includes videos coming from other datasets, namely, GSM [32], MULTIVISION [58], Princeton Tracking Benchmark [95], RGB-D Object Detection dataset [17], and UR Fall Detection Dataset [106; 107]. The 33 videos acquired by Kinect cameras span seven categories, selected to include diverse scene background modeling challenges for moving object detection: illumination changes, color and depth camouflage, intermittent motion, out of sensor depth range, color and depth shadows, and bootstrapping. Depth images are already synchronized and registered with the corresponding color images by projecting the depth map onto the color image, allowing a color-depth pixel correspondence. For each sequence, pixels that have no color-depth correspondence (due to the difference in the color and depth cameras centers) are signaled in a binary Region-of-Interest (ROI) image and are excluded by the evaluation. Other publicly available RGBD video datasets are worth mentioning, being equipped with pixel-wise GT foreground masks, which are devoted to specific applications. These include the BIWI RGBD-ID dataset [108; 109] and the IPG dataset [110; 111], targeted to people re-identification, and the VAP Trimodal People Segmentation Dataset [74; 112], that contains videos captured by thermal, depth, and color sensors, devoted to human body segmentation.