  The problem of detecting changes in a scene and segmenting the foreground
from background is still challenging, despite previous work. Moreover, new RGBD
capturing devices include depth cues, which could be incorporated to improve
foreground segmentation. In this work, we present a new nonparametric approach
where a unified model mixes the device multiple information cues. In order to
unify all the device channel cues, a new probabilistic depth data model is also
proposed where we show how handle the inaccurate data to improve foreground
segmentation. A new RGBD video dataset is presented in order to introduce a new
standard for comparison purposes of this kind of algorithms. Results show that
the proposed approach can handle several practical situations and obtain good
results in all cases.
