变得(更)真实:将眼动分类引入等方刺激的HMD实验

I. Agtzidis, M. Dorr
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引用次数: 2

摘要

眼动分类是眼动追踪研究的一个重要组成部分,从很早的时候就开始被研究。近年来,随着眼球追踪眼镜和头戴式显示器的使用,我们经历了越来越多的转向更身临其境的实验场景。然而,在这些新的场景中,大多数现有的眼动分类算法都不能再鲁棒地应用,因为它们是基于常规2D图像和视频的基于监视器的实验开发的。在本文中,我们描述了两种减少头戴式显示器中显示的360°视频的眼动分类伪影的方法。对于第一种方法,我们讨论了决策标准如何在360°视频空间中发生变化,并使用这些标准来修改文献中的五种流行算法。修改后的算法可在https://web.gin.g-node.org/ioannis.agtzidis/360_em_algorithms上公开获取。对于无法修改现有算法的情况,例如,因为它是闭源的,我们提出了第二种方法,将数据而不是算法映射到360°空间。对这两种方法的经验评估表明,它们显着减少了初始算法的伪影,特别是在远离水平中线的区域。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Getting (more) real: bringing eye movement classification to HMD experiments with equirectangular stimuli
The classification of eye movements is a very important part of eye tracking research and has been studied since its early days. Over recent years, we have experienced an increasing shift towards more immersive experimental scenarios with the use of eye-tracking enabled glasses and head-mounted displays. In these new scenarios, however, most of the existing eye movement classification algorithms cannot be applied robustly anymore because they were developed with monitor-based experiments using regular 2D images and videos in mind. In this paper, we describe two approaches that reduce artifacts of eye movement classification for 360° videos shown in head-mounted displays. For the first approach, we discuss how decision criteria have to change in the space of 360° videos, and use these criteria to modify five popular algorithms from the literature. The modified algorithms are publicly available at https://web.gin.g-node.org/ioannis.agtzidis/360_em_algorithms. For cases where an existing algorithm cannot be modified, e.g. because it is closed-source, we present a second approach that maps the data instead of the algorithm to the 360° space. An empirical evaluation of both approaches shows that they significantly reduce the artifacts of the initial algorithm, especially in the areas further from the horizontal midline.
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