提出可检测遮挡的驾驶员监控系统，提升复杂光照下的安全性。
Occlusion-aware Driver Monitoring System using the Driver Monitoring Dataset
- 分用可见光与红外图像训练独立算法，应对多传感器融合挑战
- 在真实场景中实现高精度驾驶员识别与视线区域估计，低光下仍稳定
- 首次引入鲁棒遮挡检测，符合欧标，适合车载安全系统研发者
本文提出一种鲁棒的遮挡感知驾驶员监控系统（DMS），基于驾驶员监控数据集（DMD）。系统在不同光照条件下（包括低光场景）实现驾驶员身份识别、基于区域的视线估计及人脸遮挡检测。根据欧洲新车安全评鉴协会（EuroNCAP）建议，引入遮挡检测可提升情境感知能力与系统可信度，提示性能下降时刻。系统采用分别基于可见光（RGB）和红外（IR）图像训练的算法，确保运行可靠性。文中详述了算法开发与集成流程，解决多传感器协同与实车部署难题。在DMD数据集及真实场景中的评估表明，该系统有效，其中基于RGB的模型表现更优，且首次在DMS中实现鲁棒遮挡检测，具有开创性意义。
原文摘要 · Abstract (English)
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.
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