Kurzhals Kuno
IEEE Comput Graph Appl. 2015 Jul-Aug;35(4):64-72. doi: 10.1109/MCG.2015.47. Epub 2015 May 13.
In many research fields, eye tracking has become an established method to analyze the distribution of visual attention in various scenarios. With the trend toward increasingly affordable and easy-to-use consumer hardware, we expect mobile eye tracking to become ubiquitous, recording massive amounts of gaze data on a regular basis in everyday personal situations. To make use of this data, new approaches for personal visual analytics will be necessary to make the data accessible for non-expert users for self-reflection and re-experiencing interesting events. We discuss how eye tracking fits in the context of personal visual analytics, the challenges that arise with its application to everyday situations, and the research perspectives of personal eye tracking. Therefore, the extraction and representation of areas of interest (AOIs) in the recorded data is a crucial part of data processing. We present a new technique to represent these AOIs from multiple videos: the AOI cloud. In our example, we apply this technique to examine the personal encounters of a user with other persons. The technique provides an accessible user interface that is also applicable to touch devices and therefore suitable for an integration into the everyday life of a user.
在许多研究领域，眼动追踪已成为分析各种场景下视觉注意力分布的既定方法。随着消费级硬件价格越来越亲民且易于使用的趋势，我们预计移动眼动追踪将变得无处不在，在日常个人场景中定期记录大量的注视数据。为了利用这些数据，需要新的个人视觉分析方法，以便让非专业用户能够访问数据，用于自我反思和重温有趣事件。我们讨论了眼动追踪如何融入个人视觉分析的背景，其应用于日常场景时出现的挑战，以及个人眼动追踪的研究前景。因此，在记录数据中提取和表示感兴趣区域（AOI）是数据处理的关键部分。我们提出了一种从多个视频中表示这些AOI的新技术：AOI云。在我们的示例中，我们应用此技术来检查用户与他人的个人相遇情况。该技术提供了一个易于使用的用户界面，也适用于触摸设备，因此适合集成到用户的日常生活中。
