# Background Subtraction for Moving Object Detection in RGBD Data: A Survey
## 6 Comparisons
### Comparisons on the SBM-RGBD Dataset  
Performance comparisons on the SBM-RGBD dataset are reported in Tables 8 and 9. Here, values for the RGBD-SOBS and RGB-SOBS algorithms by [PERSON] and [PERSON] [78], the SRPCA algorithm by [PERSON] et al. [77], the AvgM-D algorithm by [PERSON] and [PERSON] [113], the [PERSON] algorithm by [PERSON] [114], the SCAD algorithm by [PERSON] et al. [79], the cwisardH+ algorithm by [PERSON] and [PERSON] [76], and the MFCN algorithm by [PERSON] et al. [102], are those reported by the related authors. All the performance measures have been computed using the complete set of GTs and are available at [115]. It can be observed that the deep learning-based MFCN algorithm almost always achieves the best results in all the video categories, in terms of all the metrics. This is certainly possible thanks to the availability of such a wide dataset to train the network. Several conclusions can be drawn for each of the considered challenges by observing the remaining results. Bootstrapping can be a problem when using only color information, especially for selective background subtraction methods (e.g., RGB-SOBS), i.e., those that update the background model using only background information. Indeed, once a foreground object is erroneously included into the background model (e.g., due to inappropriate background initialization or to inaccurate segmentation of foreground objects), it will hardly be removed by the model, continuing to produce false negative results. The problem is even harder if some parts of the background are never shown during the sequences, as it happens in most of the videos of the Bootstrapping category. Indeed, in these cases, also the best performing background initialization methods [116; 117] fail and only alternative techniques (e.g., inpainting) can be adopted to recover missing data [118]. Nonetheless, depth information seems to be beneficial for affording the challenge, as reported in Table 8, where accurate results are achieved by most of the methods that exploit depth information. Figure 5: Despatx-ds video from the GSM dataset. **(a)** RGB image; **(b)** Depth image; **(c)** GT. As expected, all the methods that exploit depth information achieve high accuracy in case of color camouflage and illumination changes. In the latter case, it should be pointed out that, since this video category includes the two TimeOfDay-ds and Ls-ds sequences of the GSM dataset (without any foreground object), the performance analysis should be based on Specificity, FPR, FNR, and PWC, rather than on the other three metrics (see Section 6.4). Depth can be beneficial also for detecting and properly handling cases of intermittent motion. Indeed, foreground objects can be easily identified based on their depth, that is lower than that of the background, even when they remain stationary for long time periods. Methods that explicitly exploit this characteristic succeed in handling cases of removed and abandoned objects, achieving high accuracy. Overall, shadows do not seem to pose a strong challenge to most of the methods. Indeed, depth shadows due to moving objects cause some undefined depth values, generally close to the object contours, but these can be handled based on motion. Color shadows can be handled either exploiting depth information, that is insensitive to this challenge, or through color shadow detection techniques when only color information is taken into account. Depth camouflage and out of range (see Figure 6) are among the most challenging issues, at least when information on color is disregarded or not properly combined with depth. Indeed, even though the accuracy of most of the methods is moderately high, several false negatives are produced.