Abstract
This paper presents a robust, occlusion-aware driver monitoring system (DMS) utilizing the Driver Monitoring Dataset (DMD). The system performs driver identification, gaze estimation by regions, and face occlusion detection under varying lighting conditions, including challenging low-light scenarios. Aligned with EuroNCAP recommendations, the inclusion of occlusion detection enhances situational awareness and system trustworthiness by indicating when the system's performance may be degraded. The system employs separate algorithms trained on RGB and infrared (IR) images to ensure reliable functioning. We detail the development and integration of these algorithms into a cohesive pipeline, addressing the challenges of working with different sensors and real-car implementation. Evaluation on the DMD and in real-world scenarios demonstrates the effectiveness of the proposed system, highlighting the superior performance of RGB-based models and the pioneering contribution of robust occlusion detection in DMS.
Abstract (translated)
本文提出了一种稳健的、具有遮挡感知能力的驾驶员监控系统（DMS），该系统利用了驾驶员监测数据集（Driver Monitoring Dataset，简称DMD）。本系统能够在不同的照明条件下进行驾驶员识别、区域注视估计以及面部遮挡检测，包括挑战性的低光照场景。遵循EuroNCAP推荐标准，通过加入遮挡检测来增强情境感知和系统的可信度，以表明在某些情况下系统性能可能下降。 该系统采用分别针对RGB图像和红外（IR）图像训练的算法，从而确保其可靠运行。我们详细介绍了这些算法的发展与整合过程，以形成一个连贯的工作流程，并解决了不同传感器以及实际汽车应用中的挑战。通过DMD数据集及现实场景中对该系统的评估证明了该系统的效果，突出了基于RGB模型的优越性能和在DMS中实现稳健遮挡检测的开创性贡献。
URL
https://arxiv.org/abs/2504.20677
