# Background Subtraction for Moving Object Detection in RGBD Data: A Survey
## 3 Methods
Then, OR-PCA is applied to each of color left image and related disparity image to model the background, separately. The integration adds low-rank and sparse components obtained via OR-PCA to recover the background model and foreground mask from each image. The reconstructed sparse matrix is then thresholded to get the binary foreground mask. Finally, spatiotemporal constraints are applied to remove from the foreground mask most of the noise due to depth information. In [71], [PERSON] et al. present a method where, as an initial offline step, noise is removed from depth data based on a noise model. Background subtraction is then solved by combining RGB and depth features, both modeled by MoG. The fundamental idea in their combination strategy is that when depth measurement is reliable, the segmentation is mainly based on depth information; otherwise, RGB is used as an alternative. [PERSON] et al. [72] propose a MoG model for color information and a single Gaussian model for depth, together with a color-depth consistency check mechanism driving the updating of the two models. However, experimental results aim at evaluating background estimation, rather than background subtraction. [PERSON] et al. [73] propose a depth-weighted group-wise PCA-based algorithm, named DG-PCA. The background/foreground separation problem is formulated as a weighted L\({}_{2,1}\)-norm PCA problem with depth-based group sparsity being introduced. Dynamic groups are first generated solely based on depth, and then an iterative solution using depth to define the weights in L\({}_{2,1}\)-norm is developed. The method handles moving cameras through global motion compensation. In [19], [PERSON] et al. present a method where two separate color and depth background models are based on ViBe, and the two resulting foreground masks are fused by weighted average. The result is further adaptively refined, taking into account multi-cue information (color, depth, and edges) and spatiotemporal consistency (in the neighborhood of foreground pixels in the actual and previous frames). In [20], [PERSON] et al. propose a method to segment foreground objects based on color and depth data independently, using an existing background subtraction method (in the experiments they choose MoG). They focus on refining the inaccurate results through supervised learning. They extract several features from the source color and depth data in the foreground areas. These features are fed to two independent classifiers (in the experiments they choose random forest [83]) to obtain a better foreground detection. In [74], [PERSON] et al. propose a baseline algorithm for human body segmentation using color, depth, and thermal information. To reduce the spatial search space in subsequent steps, the preliminary step is background subtraction, achieved in the depth domain using MoG. In the method proposed by [PERSON] et al. [75], named SAFBS (Self-Adapting Fuzzy Background Subtraction), background subtraction is based on two background models for color (in the HSV color space) and depth, providing an initial foreground segmentation by frame differencing. A fuzzy algorithm computes the membership value of each pixel to background or foreground, based on color and depth differences, as well as depth similitude, of the current frame and the background. Temporal and spatial smoothing of the membership values is applied to reduce false alarms due to depth flickering and imprecise measurements around object contours, respectively. The classification result is then employed to update the two background models, using automatically computed learning rates. [PERSON] and [PERSON] [76] adapt an existing background modeling method using the WiSARD weightless neural network (WNNs) [41] to the domain of RGBD videos. Color and depth video streams are synchronously but separately modeled by WNNs at each pixel, using a set of initial video frames for network training.