In-Cabin vehicle synthetic data to test Deep Learning based human pose estimation models.

The use of vehicle in-cabin monitoring has been increasing to fulfil the specifications of European safety regulations. These regulations present several requirements for detecting driver distraction, and more complex requirements are soon to be expected in higher automation levels. Today's restraint systems provide optimal protection in standard frontal seat positions and deviations to this might cause severe airbag-induced injuries. This makes in-cabin monitoring critical to improve safety and mitigate dangerous situations in case of a crash, and especially in high levels of autonomous driving. Defining the best sensor positioning inside the vehicle's cabin is a challenge due to its constraints and limitations. The main aim of this work was to verify if simulated 3D human models integrated into a 3D modelled vehicle interior environment can be used to run Deep Learning based human pose estimation models. To perform such task, we utilized the software MakeHuman combined with Blender, to build the virtual environment and create photorealistic scenes containing selected front occupants' postures use cases and then feed into Openpose and Mask R-CNN models. The results showed that using a 2D HPE (Human Pose Estimation) network pre-trained on real data, can detect successfully photorealistic synthetic data of humans under complex scenarios. It is also shown that complex and rare postures can cause failure on 2D HPE detections, as shown in the literature review. This work helps to define the most suitable camera positions which, in combination with specific camera lenses, can deliver quality images for a robust pose detection.