Automated analysis of eye-tracker-based human-human interaction studies

• URL: http://arxiv.org/abs/2007.04671v1
• Date: Thu, 9 Jul 2020 10:00:03 GMT
• Title: Automated analysis of eye-tracker-based human-human interaction studies
• Authors: Timothy Callemein, Kristof Van Beeck, Geert Br\^one, Toon Goedem\'e
• Abstract summary: We investigate which state-of-the-art computer vision algorithms may be used to automate the post-analysis of mobile eye-tracking data. For the case study in this paper, we focus on mobile eye-tracker recordings made during human-human face-to-face interactions. We show that the use of this single-pipeline framework provides robust results, which are both more accurate and faster than previous work in the field.
• Score: 2.433293618209319
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Mobile eye-tracking systems have been available for about a decade now and are becoming increasingly popular in different fields of application, including marketing, sociology, usability studies and linguistics. While the user-friendliness and ergonomics of the hardware are developing at a rapid pace, the software for the analysis of mobile eye-tracking data in some points still lacks robustness and functionality. With this paper, we investigate which state-of-the-art computer vision algorithms may be used to automate the post-analysis of mobile eye-tracking data. For the case study in this paper, we focus on mobile eye-tracker recordings made during human-human face-to-face interactions. We compared two recent publicly available frameworks (YOLOv2 and OpenPose) to relate the gaze location generated by the eye-tracker to the head and hands visible in the scene camera data. In this paper we will show that the use of this single-pipeline framework provides robust results, which are both more accurate and faster than previous work in the field. Moreover, our approach does not rely on manual interventions during this process.

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