@inproceedings{b89cbc35484148318192577e6e7e2a8d,
title = "PuReST: Robust pupil tracking for real-time pervasive eye tracking",
abstract = "Pervasive eye-tracking applications such as gaze-based human computer interaction and advanced driver assistance require real-time, accurate, and robust pupil detection. However, automated pupil detection has proved to be an intricate task in real-world scenarios due to a large mixture of challenges – for instance, quickly changing illumination and occlusions. In this work, we introduce the Pupil Reconstructor with Subsequent Tracking (PuReST), a novel method for fast and robust pupil tracking. The proposed method was evaluated on over 266,000 realistic and challenging images acquired with three distinct head-mounted eye tracking devices, increasing pupil detection rate by 5.44 and 29.92 percentage points while reducing average run time by a factor of 2.74 and 1.1. w.r.t. state-of-the-art 1) pupil detectors and 2) vendor provided pupil trackers, respectively. Overall, PuReST outperformed other methods in 81.82% of use cases.",
keywords = "Embedded, Eye tracking, Open source, Pervasive, Pupil detection, Pupil tracking, Real-time",
author = "Thiago Santini and Wolfgang Fuhl and Enkelejda Kasneci",
note = "Publisher Copyright: {\textcopyright} 2018 Association for Computing Machinery.; 10th ACM Symposium on Eye Tracking Research and Applications, ETRA 2018 ; Conference date: 14-06-2018 Through 17-06-2018",
year = "2018",
month = jun,
day = "14",
doi = "10.1145/3204493.3204578",
language = "English",
series = "Eye Tracking Research and Applications Symposium (ETRA)",
publisher = "Association for Computing Machinery",
editor = "Spencer, {Stephen N.}",
booktitle = "Proceedings - ETRA 2018",
}