A system for the generation of in-car human body pose datasets

With the advent of autonomous vehicles, detection of the occupants’ posture is crucial to tackle the needs of infotainment interaction or passive safety systems. Generative approaches have been recently proposed for human body pose in-car detection, but this type of approaches requires a large training dataset for a feasible accuracy. This requirement poses a difficulty, given the substantial time required to annotate such large amount of data. In the in-car scenario, this requirement risk increases even further, since a robust human body pose ground-truth system capable of working in it is needed but inexistent. Currently, the gold standard for human body pose capture is based on optical systems, requiring up to 39 visible markers for a plug-in gait model, which in this case are not feasible given the occlusions inside the car. Other solutions, such as inertial suits, also have limitations linked to magnetic sensitivity and global positioning drift. In this paper, a system for the generation of images for human body pose detection in an in-car environment is proposed. To this end, we propose to smartly combine inertial and optical systems to suppress their individual limitations: By combining the global positioning of 3 visible head markers provided by the optical system with the inertial suit’s relative human body pose, we obtain an occlusion-ready, drift-free full-body global positioning system. This system is then spatially and temporally calibrated with a time-of-flight sensor, automatically obtaining in-car image data with (multi-person) pose annotations. Besides quantifying the inertial suit inherent sensitivity and accuracy, the feasibility of the overall system for human body pose capture in the in-car scenario was demonstrated. Our results quantify the errors associated with the inertial suit, pinpoint some sources of the system’s uncertainty and propose how to minimize some of them. Finally, we demonstrate the feasibility of using system generated data (which was made publicly available), independently or mixed with two publicly available generic datasets (not in-car), to train 2 machine learning algorithms, demonstrating the improvement in their algorithmic accuracy for the in-car scenario.