Automated analysis of eye-tracker-based human-human interaction studies
This work addresses the lack of robust software for analyzing mobile eye-tracking data, which is incremental for fields like marketing, sociology, usability studies, and linguistics.
The paper tackled the problem of automating post-analysis of mobile eye-tracking data in human-human interactions by comparing YOLOv2 and OpenPose frameworks to relate gaze to head and hands, resulting in a single-pipeline framework that is more accurate and faster than previous methods without manual intervention.
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.