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

TL;DR

This paper explores the use of computer vision algorithms to automate the analysis of mobile eye-tracking data during face-to-face interactions, improving accuracy and efficiency without manual intervention.

Contribution

It demonstrates that a single-pipeline framework using YOLOv2 and OpenPose enhances robustness, speed, and accuracy in analyzing mobile eye-tracking data compared to prior methods.

Findings

The framework provides more accurate gaze relation analysis.

It is faster than previous manual or semi-automated methods.

The approach eliminates the need for manual interventions.

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.