Interdisciplinary Center for Security, Reliability and Trust (SnT), University of Luxembourg, L-4364 Esch-sur-Alzette, Luxembourg.
National Research Council of Italy-Institute of Applied Sciences and Intelligent Systems, 73100 Lecce, Italy.
Sensors (Basel). 2020 Jul 3;20(13):3739. doi: 10.3390/s20133739.
The automatic detection of eye positions, their temporal consistency, and their mapping into a line of sight in the real world (to find where a person is looking at) is reported in the scientific literature as gaze tracking. This has become a very hot topic in the field of computer vision during the last decades, with a surprising and continuously growing number of application fields. A very long journey has been made from the first pioneering works, and this continuous search for more accurate solutions process has been further boosted in the last decade when deep neural networks have revolutionized the whole machine learning area, and gaze tracking as well. In this arena, it is being increasingly useful to find guidance through survey/review articles collecting most relevant works and putting clear pros and cons of existing techniques, also by introducing a precise taxonomy. This kind of manuscripts allows researchers and technicians to choose the better way to move towards their application or scientific goals. In the literature, there exist holistic and specifically technological survey documents (even if not updated), but, unfortunately, there is not an overview discussing how the great advancements in computer vision have impacted gaze tracking. Thus, this work represents an attempt to fill this gap, also introducing a wider point of view that brings to a new taxonomy (extending the consolidated ones) by considering gaze tracking as a more exhaustive task that aims at estimating gaze target from different perspectives: from the eye of the beholder (first-person view), from an external camera framing the beholder's, from a third-person view looking at the scene where the beholder is placed in, and from an external view independent from the beholder.
眼动位置的自动检测、它们的时间一致性，以及将其映射到现实世界中的视线（以找到人在看哪里）在科学文献中被报道为眼动追踪。在过去几十年中，计算机视觉领域中，这已经成为一个非常热门的话题，并且具有惊人的、不断增长的应用领域数量。从最初的开创性工作开始，已经走过了漫长的道路，在过去十年中，随着深度学习神经网络彻底改变了整个机器学习领域，这种对更准确解决方案的持续探索也得到了进一步推动，眼动追踪也是如此。在这个领域中，通过收集最相关工作并明确现有技术的优缺点，同时引入精确的分类法的调查/综述文章越来越有用。这种手稿可以帮助研究人员和技术人员选择更好的方法来实现他们的应用或科学目标。在文献中，存在整体的和专门的技术调查文件（即使没有更新），但不幸的是，没有一篇文章讨论计算机视觉的巨大进步如何影响眼动追踪。因此，这项工作试图填补这一空白，同时通过将眼动追踪视为一个更全面的任务来引入更广泛的观点，该任务旨在从不同的角度估计注视目标：从观察者的眼睛（第一人称视角）、从注视者的外部摄像机框架、从第三个人的视角观察观察者所处的场景，以及从独立于观察者的外部视角。
