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.

随着自动驾驶汽车的出现，对乘员姿势的检测对于解决信息娱乐交互或被动安全系统的需求至关重要。最近提出了用于人体姿势车载检测的生成方法，但是这种方法需要大量的训练数据集以实现可行的准确性。鉴于注释大量数据需要大量时间，因此此要求带来了困难。在车内场景中，此需求风险会进一步增加，因为需要但不存在能够在其中工作的坚固的人体姿势地面真假系统。当前，用于人体姿势捕获的黄金标准基于光学系统，对于插入式步态模型需要多达39个可见标记，在这种情况下，考虑到车内的闭塞，这是不可行的。其他解决方案 惯性套装等也具有与磁灵敏度和全球定位漂移相关的局限性。在本文中，提出了一种在车内环境中用于人体姿势检测的图像生成系统。为此，我们建议将惯性系统和光学系统巧妙地结合起来，以消除其各自的局限性：通过将光学系统提供的3个可见头部标记的全局定位与惯性服的相对人体姿势相结合，我们可以进行遮挡，无漂移全身全球定位系统。然后用飞行时间传感器对该系统进行空间和时间校准，从而自动获得带有（多人）姿势注释的车内图像数据。除了量化惯性服固有的灵敏度和准确性外，证明了整个系统在车内场景中捕获人体姿势的可行性。我们的结果量化了与惯性服相关的误差，查明了系统不确定性的一些来源，并提出了如何使其中一些最小化的方法。最后，我们演示了使用系统生成的数据（可公开获得）（独立或与两个可公开获得的通用数据集（非车载）混合）训练2种机器学习算法的可行性，证明了它们在算法准确性方面的改进车内场景。