  In order to ensure safe autonomous driving, precise information about the
conditions in and around the vehicle must be available. Accordingly, the
monitoring of occupants and objects inside the vehicle is crucial. In the
state-of-the-art, single or multiple deep neural networks are used for either
object recognition, semantic segmentation, or human pose estimation. In
contrast, we propose our Multitask Detection, Segmentation and Pose Estimation
Network (MDSP) -- the first multitask network solving all these three tasks
jointly in the area of occupancy monitoring. Due to the shared architecture,
memory and computing costs can be saved while achieving higher accuracy.
Furthermore, our architecture allows a flexible combination of the three
mentioned tasks during a simple end-to-end training. We perform comprehensive
evaluations on the public datasets SVIRO and TiCaM in order to demonstrate the
superior performance.
