POnline: An Online Pupil Annotation Tool Employing Crowd-sourcing and Engagement Mechanisms

David Gil de Gómez Pérez, R. Bednarik
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引用次数: 2

Abstract

Pupil center and pupil contour are two of the most important features in the eye-image used for video-based eye-tracking. Well annotated databases are needed in order to allow benchmarking of the available- and new pupil detection and gaze estimation algorithms. Unfortunately, creation of such a data set is costly and requires a lot of efforts, including manual work of the annotators. In addition, reliability of manual annotations is hard to establish with a low number of annotators. In order to facilitate progress of the gaze tracking algorithm research, we created an online pupil annotation tool that engages many users to interact through gamification and allows utilization of the crowd power to create reliable annotations \cite{artstein2005bias}. We describe the tool and the mechanisms employed, and report results on the annotation of a publicly available data set. Finally, we demonstrate an example utilization of the new high-quality annotation on a comparison of two state-of-the-art pupil center algorithms.
POnline:使用众包和参与机制的在线学生注释工具
瞳孔中心和瞳孔轮廓是眼球图像中两个最重要的特征,用于视频眼动追踪。为了对现有的和新的瞳孔检测和凝视估计算法进行基准测试,需要有良好注释的数据库。不幸的是,创建这样一个数据集的成本很高,并且需要大量的工作,包括注释器的手工工作。此外,在注释者数量较少的情况下,很难建立手动注释的可靠性。为了促进注视跟踪算法研究的进展,我们创建了一个在线瞳孔注释工具,通过游戏化吸引许多用户进行交互,并允许利用人群力量创建可靠的注释\cite{artstein2005bias}。我们描述了所使用的工具和机制,并报告了对公开可用数据集的注释结果。最后,我们展示了在两种最先进的瞳孔中心算法的比较上使用新的高质量注释的示例。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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