Schöning Julius, Gundler Christopher, Heidemann Gunther, König Peter, Krumnack Ulf
Institute of Cognitive Science, Osnabrück University, Germany.
J Eye Mov Res. 2017 Nov 20;10(5). doi: 10.16910/jemr.10.5.4.
With the increasing number of studies, where participants' eye movements are tracked while watching videos, the volume of gaze data records is growing tremendously. Unfortunately, in most cases, such data are collected in separate files in custom-made or proprietary data formats. These data are difficult to access even for experts and effectively inaccessible for non-experts. Normally expensive or custom-made software is necessary for their analysis. We address this problem by using existing multimedia container formats for distributing and archiving eye-tracking and gaze data bundled with the stimuli data. We define an exchange format that can be interpreted by standard multimedia players and can be streamed via the Internet. We convert several gaze data sets into our format, demonstrating the feasibility of our approach and allowing to visualize these data with standard multimedia players. We also introduce two VLC player add-ons, allowing for further visual analytics. We discuss the benefit of gaze data in a multimedia container and explain possible visual analytics approaches based on our implementations, converted datasets, and first user interviews.
随着越来越多的研究在参与者观看视频时追踪其眼动，注视数据记录的数量正在急剧增长。不幸的是，在大多数情况下，此类数据是以定制或专有数据格式存储在单独的文件中。即使对于专家来说，这些数据也难以访问，对于非专家而言则几乎无法访问。通常需要昂贵的或定制的软件来进行分析。我们通过使用现有的多媒体容器格式来分发和存档与刺激数据捆绑在一起的眼动追踪和注视数据，从而解决了这个问题。我们定义了一种可由标准多媒体播放器解释并可通过互联网流式传输的交换格式。我们将多个注视数据集转换为我们的格式，证明了我们方法的可行性，并允许使用标准多媒体播放器对这些数据进行可视化。我们还引入了两个VLC播放器插件，以实现进一步的视觉分析。我们讨论了多媒体容器中注视数据的优势，并基于我们的实现、转换后的数据集和首次用户访谈，解释了可能的视觉分析方法。
