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In order to ensure safe autonomous driving, precise information about theconditions in and around the vehicle must be available. Accordingly, themonitoring of occupants and objects inside the vehicle is crucial. In thestate-of-the-art, single or multiple deep neural networks are used for eitherobject recognition, semantic segmentation, or human pose estimation. Incontrast, we propose our Multitask Detection, Segmentation and Pose EstimationNetwork (MDSP) -- the first multitask network solving all these three tasksjointly 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 threementioned tasks during a simple end-to-end training. We perform comprehensiveevaluations on the public datasets SVIRO and TiCaM in order to demonstrate thesuperior performance.

Multitask Network for Joint Object Detection, Semantic Segmentation and Human Pose Estimation in Vehicle Occupancy Monitoring