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.
翻译:利用各种计算机视觉技术,对先前的分离眼部部件进行了电离层装配,这是学生或电离跟踪基于视频视谱学的基本组成部分。有几个因素,例如眼皮形状、摄像头姿势或睫毛造成的隔绝,经常断裂椭圆形装配算法,依赖定义明确的学生或虹膜边缘部分。在这项工作中,我们提议培训一个革命神经网络,直接分割整个椭圆结构,并表明这一框架对于隔离非常有力,并且为学生和虹膜跟踪性能提供了优异的功能(在双像素误差边缘内,学生和虹膜中心检测率分别至少增加10美元和24美元),而对于多种公开的合成分解数据集而言,则使用标准眼部分割法。