论文标题
Ellseg：椭圆形分段框架，用于强大的凝视跟踪
EllSeg: An Ellipse Segmentation Framework for Robust Gaze Tracking
论文作者
论文摘要
椭圆拟合是基于瞳孔或虹膜跟踪视频眼镜学中的重要组成部分，是对使用各种计算机视觉技术生成的先前分段的眼部零件进行的。几个因素，例如由于眼睑形状，相机位置或睫毛引起的遮挡，经常打破依赖定义明确的学生或虹膜边缘段的椭圆形拟合算法。在这项工作中，我们建议培训一个卷积神经网络，以直接分割整个椭圆结构，并证明这种框架对闭塞是可靠的，并提供了卓越的学生和Iris跟踪性能（至少10 $ \％$ \％$ \％$ \％$ \％$ \％$ \％$ $ \％$ $ \％$在使用标准眼睛的二级型号中，分别在学生中的学生和IRIS中心的检测率增加了。
Ellipse fitting, an essential component in pupil or iris tracking based video oculography, is performed on previously segmented eye parts generated using various computer vision techniques. Several factors, such as occlusions due to eyelid shape, camera position or eyelashes, frequently break ellipse fitting algorithms that rely on well-defined pupil or iris edge segments. In this work, we propose training a convolutional neural network to directly segment entire elliptical structures and demonstrate that such a framework is robust to occlusions and offers superior pupil and iris tracking performance (at least 10$\%$ and 24$\%$ increase in pupil and iris center detection rate respectively within a two-pixel error margin) compared to using standard eye parts segmentation for multiple publicly available synthetic segmentation datasets.
