# Background Subtraction for Moving Object Detection in RGBD Data: A Survey
## 3 Methods
For each pixel, the support of each classifier to the final segmentation result is obtained by considering the global edge-closeness probability and the classification labels obtained in the previous frame. In [62], the authors improve their method, proposing a method named MoG-RegPRE, that builds not only pixel-based but also region-based models from depth and color data, and fuses the models in a mixture of experts fashion to improve the final foreground detection performance. [PERSON] et al. [63] adopt RGBD streams for recognizing gait patterns of individuals. To extract RGBD information of moving objects, they adopt the SOBS model for color background subtraction and use the obtained foreground masks to extract the depth information of people silhouettes from the registered depth frames. In [18], [PERSON] and [PERSON] present a foreground segmentation system that combines color and depth information captured by a Kinect camera to perform a complete Bayesian segmentation between foreground and background classes. The system adopts a combination of spatial-color and spatial-depth region-based MoG models for the foreground, as well as two color and depth pixel-wise MoG models for the background, in a Logarithmic Opinion Pool decision framework used to combine the likelihoods of each model correctly. A post-processing step based on a trimap analysis is also proposed to correct the precision errors that the depth sensor introduces in the object contour. The algorithm proposed by [PERSON] et al. in [64] explicitly models the scene background and foreground with a KDE approach in a quantized x-y-hue-saturation-depth space. Foreground segmentation is achieved by thresholding the log-likelihood ratio over the background and foreground probabilities. [PERSON] et al. [65] propose an extension of the Codebook model. Similarly to CB4D [58], it includes depth as a fourth channel of the background model but also applies colorimetric invariants to modify the color aspect of the input images, to give them the aspect they would have under canonical illuminants. In [66], [PERSON] et al. model grayscale color and depth values based on MoG. The combination of the two models is based on the product of the likelihoods of the two models. [PERSON] et al. [67] initially exploit depth information to achieve a coarse segmentation, using middleware of the adopted ASUS Xtion camera. The obtained mask is refined in uncertain areas (mainly object contours) having high background/foreground contrast, locally modeling colors by their mean value. [PERSON] et al. [68] adapt to Kinect data a matting method previously proposed for ToF data. It is based on Otsu thresholding of the depth map and region growing for labeling pixel clusters, assembled to create an alpha map. Edge improvement is obtained by logical OR of the current map with those of the previous four frames. [PERSON] et al. [69] propose a post-processing framework based on an initial segmentation obtained solely by depth data. Two post-processing steps are proposed: a foreground hole detection step and object boundary refining step. For foreground hole detection, they obtain two weak decisions based on the region color cue and the contour contrast cue, adaptively fused according to their corresponding reliability. For object boundary refinement, they apply weighted fusion of motion probability weighted temporal prior, color likelihood, and smoothness constraints. Therefore, besides handling challenges such as color camouflage, illumination variations, and shadows, the method maintains spatial and temporal consistency of the obtained segmentation, a fundamental issue for the telepresence target application. [PERSON] et al. [70] propose the DEOR-PCA (Depth Extended Online RPCA) method for background subtraction using binocular cameras. It consists of four main stages: disparity estimation, background modeling, integration, and spatiotemporal constraints. Initially, the range information is obtained using disparity estimation algorithms on a set of stereo pairs.