# Background Subtraction for Moving Object Detection in RGBD Data: A Survey
## 3 Methods
In the detection phase, classification is interleaved with re-training on current colors whenever pixels are detected as belonging to the background. Finally, the obtained output masks are combined by an OR operator and post-processed by morphological filtering. [PERSON] et al. [77] investigate the performance of an online RPCA-based method, named SRPCA, for moving object detection using RGBD videos. The algorithm consists of three main stages: (i) detection of dynamic images to create an input dynamic sequence by discarding motionless video frames; (ii) computation of spatiotemporal graph Laplacians; and (iii) application of RPCA to incorporate the preceding two steps for the separation of background and foreground components. In the experiments, the algorithm is tested by using only intensity, only RGB, and RGBD features, leading to the surprising conclusion that best results are achieved using only intensity features. The algorithm proposed by [PERSON] and [PERSON] [78], named RGBD-SOBS, is based on two background models for color and depth information, exploiting a self-organizing neural background model previously adopted for RGB videos [84]. The resulting color and depth detection masks are combined, not only to achieve the final results but also to better guide the selective model update procedure. [PERSON] et al. [79] propose an algorithm, named SCAD, based on a simple combination of the appearance (color) and depth information. The depth background is obtained using, for each pixel, its farthest depth value along the whole video, thus resulting in a batch algorithm. The likelihood of the appearance background is computed using texture-based and RGB-based background subtraction. To reduce false positives due to illumination changes, SCAD roughly detects foreground objects by using texture-based background subtraction. Then, it performs RGB-based background subtraction to improve the results of texture-based background subtraction. Finally, foreground masks are obtained using graph cuts to optimize an energy function which combines the two likelihoods of the background. [PERSON] et al. [32] construct a scene background model using KDE with a three-dimensional Gaussian kernel. One of the dimensions models depth information, while the other two model normalized chromaticity coordinates. Missing depth data are modeled using a probabilistic strategy to distinguish pixels that belong to the background model from those which are due to foreground objects. Pixels that cannot be classified as background or foreground are placed in the undefined class. Two different implementations are obtained depending on whether undefined pixels are considered as background (GSM\({}_{UB}\)) or foreground (GSM\({}_{UF}\)), demonstrating their suitability for scenes where actions happen far or close to the sensor, respectively. [PERSON] et al. [80] propose the RGBD-KDE algorithm, also based on a scene background model using KDE, but using a two-dimensional Gaussian kernel. One of the dimensions models depth information, while the other models the intensity (average of RGB components). To reduce computational complexity, the Fast Gaussian Transform is adapted to the problem. [PERSON] et al. [81] construct color and depth models based on ViBe and fuse the results in a weighting mechanism for the model update that relies on depth reliability.