# Background Subtraction for Moving Object Detection in RGBD Data: A Survey
## 3 Methods  
In the last twenty years, several methods have been proposed for background subtraction exploiting depth data, as an alternative or complement to color data. A summary of background subtraction methods for RGBD videos is given in Table 1. Here, apart from the name of the authors and the related reference (column Authors and Ref. ), we report (column Used data) whether they exploit only the depth information (D) or the complementary nature of color and depth information (RGBD). Moreover, we specify (column Depth data) how the considered depth data is acquired (Kinect, ToF cameras, stereo vision devices). Furthermore, we specify (column Model) the type of model adopted for the background, including Codebook [34], Frame difference, Kernel Density Estimation (KDE) [35], Mixture of Gaussians (MoG) [36], Robust Principal Components Analysis (RPCA) [37], Self-Organizing Background Subtraction (SOBS) [38], Single Gaussian [39], Thresholding, ViBe [40], and WiSARD weightless neural network [41]. Finally, we specify (column No. of models) if they extend to RGBD data well-known background models originally designed for color data (1 model) or model the scene background based on color and depth independently and then combine the results, on the basis of different criteria (2 models). In the following, we provide a brief description of the reviewed methods, presented in chronological order. In case of research dealing with higher-level systems (e.g., teleconferencing, matting, fall detection, human tracking, gesture recognition, object detection), we limit our attention to background modeling and foreground detection. [PERSON] et al. [6] present a method of statistical background modeling for stereo sequences based on the disparity images extracted from stereo pairs. The depth background is modeled by a single Gaussian, similarly to [39], but selective update prevents the incorporation of foreground objects into the background. The method proposed by [PERSON] et al. [42] is an adaptation of the MoG algorithm to color and depth data obtained with a stereo device. Each background pixel is modeled as a mixture of four-dimensional Gaussian distributions: three components are the color data (the YUV color space components), and the fourth one is the depth data. Color and depth features are considered independent, and the same updating strategy of the original MoG algorithm is used to update the distribution parameters. The authors propose a strategy where, for reliable depth data, depth-based decisions bias the color-based ones: in case that a reliable distribution match is found in the depth component, the color-based matching criterion is relaxed, thus reducing the color camouflage errors. When the stereo matching algorithm is not reliable, the color-based matching criterion is set to be harder to avoid problems such as shadows or local illumination changes. [PERSON] et al. [43] propose an approach based on stereo vision, which uses the disparity (estimated offline) to warp one image of the pair in the other one, thus creating a geometric background model. If the color and brightness between corresponding points do not match, the pixels either belong to a foreground object or to an occlusion shadow. The latter case can be further disambiguated using more than two camera views. [PERSON] et al. [16] propose a foreground segmentation method using the YUV color space with the additional depth values estimated by stereo cameras. They adopt four-dimensional MoG models, also modulating the background model learning rate based on scene activity and making color-based segmentation criteria dependent on depth observations. [PERSON] et al. [44] describe two algorithms for bi-layer segmentation fusing stereo and color/contrast information, focused on live background substitution for teleconferencing. To segment the foreground, this approach relies on stereo vision, assuming that people participating in the teleconference are close to the camera. Color information is used to cope with stereo occlusion and low-texture regions.