# Background Subtraction for Moving Object Detection in RGBD Data: A Survey
## 3 Methods
The depth data provided by a ToF camera is used to generate 3D-TV contents by [PERSON] et al. [7]. The MoG algorithm is applied to the depth data to obtain foreground regions, which are then excluded by median filtering to improve background depth map accuracy. In [47], [PERSON] et al. propose a multi-camera system that combines color and depth data, obtained with a low-resolution ToF camera, for video segmentation. The algorithm applies the ViBe algorithm independently to the color and the depth data. The obtained foreground masks are then combined with logical operations and post-processed with morphological operations. MoG is also adopted in the algorithm proposed by [PERSON] et al. [48], where depth and infrared data captured by a ToF camera are combined to detect foreground objects. Two independent background models are built, and each pixel is classified as background or foreground only if the two models matching conditions agree. Very close or overlapping foreground objects are further separated using a depth gradient-based segmentation. [PERSON] et al. [49] propose ToFCut, an algorithm that combines color and depth cues in a unified probabilistic fusion framework and a novel adaptive weighting scheme to control the influence of these two cues intelligently over time. Bilayer segmentation is formulated as a binary labeling problem, whose optimal solution is obtained by minimizing an energy function. The data term evaluates the likelihood of each pixel belonging to the foreground or the background. The contrast term encodes the assumption that segmentation boundaries tend to align with the edges of high contrast. Color and depth foreground and background pixels are modeled through MoGs and single Gaussians, respectively, and their weighting factors are adaptively adjusted based on the discriminative capabilities of their models. The algorithm is also used in an automatic matting system [82] to automatically generate foreground masks, and consequently trimaps, to guide alpha matting. [PERSON] et al. [50] propose a matting method using the intensity map generated by ToF cameras. It first segments the distance map based on the corresponding values of the intensity map and then applies region growing to the filtered distance map, to identify and label pixel clusters. A trimap is obtained by eroding the output to select the foreground, dilating it to select foreground, and selecting as indeterminate the remaining contour pixels. The obtained trimap is fed in input to a matting algorithm that refines the result. [PERSON] et al. [51] use a thresholding technique to separate the foreground from the background in multiple planes of the video volume, for the generation of 3D-TV contents. A posterior trimap-based refinement using hierarchical graph cuts segmentation is further adopted to reduce the artifacts produced by the depth noise. [PERSON] et al. [52] employ stereo cameras to extract pedestrians. Foreground regions are extracted by MoG-based background subtraction and shadow detection using the color data. Then the moving objects are extracted by thresholding the histogram of depth data, computed by stereo matching. [PERSON] et al. [53] exploit the information captured by a multi-sensor system consisting of a stereo camera pair with a ToF range sensor. Motion, retrieved by color and depth frame difference, provides the initial ROI mask. The foreground mask is first extracted by region growing in the depth data, where seeds are obtained by the ROI, then refined based on color edges. Finally, a trimap is generated, where uncertain values are those along the foreground contours, and are classified based on color in the CIELab color space. [PERSON] et al. [54] explore the Kinect sensor for the application of detecting falls in the elderly. For people detection, the system adopts a single Gaussian depth background model. [PERSON] and [PERSON] [55] propose an approach to video matting that combines color information with the depth provided by ToF cameras.