This study proposes a pupil-tracking method applicable to drivers both with and without sunglasses on, which has greater compatibility with augmented reality (AR) three-dimensional (3D) head-up displays (HUDs). Performing real-time pupil localization and tracking is complicated by drivers wearing facial accessories such as masks, caps, or sunglasses. The proposed method fulfills two key requirements: low complexity and algorithm performance. Our system assesses both bare and sunglasses-wearing faces by first classifying images according to these modes and then assigning the appropriate eye tracker. For bare faces with unobstructed eyes, we applied our previous regression-algorithm-based method that uses scale-invariant feature transform features. For eyes occluded by sunglasses, we propose an eye position estimation method: our eye tracker uses nonoccluded face area tracking and a supervised regression-based pupil position estimation method to locate pupil centers. Experiments showed that the proposed method achieved high accuracy and speed, with a precision error of <10 mm in <5 ms for bare and sunglasses-wearing faces for both a 2.5 GHz CPU and a commercial 2.0 GHz CPU vehicle-embedded system. Coupled with its performance, the low CPU consumption (10%) demonstrated by the proposed algorithm highlights its promise for implementation in AR 3D HUD systems.

中文翻译：

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无需眼镜的3D HUD的戴眼镜的低复杂度学生跟踪

这项研究提出了一种瞳孔追踪方法，该方法适用于戴或不戴墨镜的驾驶员，该方法与增强现实（AR）三维（3D）平视显示器（HUD）具有更大的兼容性。驾驶员戴着诸如面罩，帽子或太阳镜之类的面部配件，从而进行实时的瞳孔定位和跟踪变得很复杂。所提出的方法满足两个关键要求：低复杂度和算法性能。我们的系统通过首先根据这些模式对图像进行分类，然后分配适当的眼动仪，来评估裸露的脸和戴太阳眼镜的脸。对于眼睛通畅的裸露脸，我们应用了之前基于回归算法的方法，该方法使用了尺度不变特征变换特征。对于太阳镜遮挡的眼睛，我们提出了一种眼睛位置估计方法：我们的眼动仪使用非遮挡脸部区域跟踪和基于监督的基于回归的瞳孔位置估计方法来定位瞳孔中心。实验表明，该方法实现了高精度和高速度，对于2.5 GHz CPU和商用2.0 GHz CPU车载系统，裸脸和太阳镜面的精度误差均在<5 ms内，小于10 mm。结合其性能，该算法证明了较低的CPU消耗（10％），突显了其在AR 3D HUD系统中实现的希望。5 GHz CPU和商用2.0 GHz CPU车载系统。结合其性能，该算法证明了较低的CPU消耗（10％），突显了其在AR 3D HUD系统中实现的希望。5 GHz CPU和商用2.0 GHz CPU车载系统。结合其性能，该算法证明了较低的CPU消耗（10％），突显了其在AR 3D HUD系统中实现的希望。