  To ensure safety in automated driving, the correct perception of the
situation inside the car is as important as its environment. Thus, seat
occupancy detection and classification of detected instances play an important
role in interior sensing. By the knowledge of the seat occupancy status, it is
possible to, e.g., automate the airbag deployment control. Furthermore, the
presence of a driver, which is necessary for partially automated driving cars
at the automation levels two to four can be verified. In this work, we compare
different statistical methods from the field of image segmentation to approach
the problem of background-foreground segmentation in camera based interior
sensing. In the recent years, several methods based on different techniques
have been developed and applied to images or videos from different
applications. The peculiarity of the given scenarios of interior sensing is,
that the foreground instances and the background both contain static as well as
dynamic elements. In data considered in this work, even the camera position is
not completely fixed. We review and benchmark three different methods ranging,
i.e., Gaussian Mixture Models (GMM), Morphological Snakes and a deep neural
network, namely a Mask R-CNN. In particular, the limitations of the classical
methods, GMM and Morphological Snakes, for interior sensing are shown.
Furthermore, it turns, that it is possible to overcome these limitations by
deep learning, e.g.\ using a Mask R-CNN. Although only a small amount of ground
truth data was available for training, we enabled the Mask R-CNN to produce
high quality background-foreground masks via transfer learning. Moreover, we
demonstrate that certain augmentation as well as pre- and post-processing
methods further enhance the performance of the investigated methods.
