Learning-based Event-based Human Gaze Tracking with Blink Detection

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egve20241367.pdf (408.71 KB)
Date
2024
Authors
Kanno, Mao
Isogawa, Mariko
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
This paper proposes an eye-tracking system using a CNN-LSTM network that utilizes only event data. This method holds potential for future applications in a wide range of fields, including AR/VR headsets, healthcare, and sports. Compared to traditional frame-based camera methods, our proposed approach achieves high FPS and low power consumption by utilizing event cameras. To improve the estimation accuracy, our gaze estimation system incorporates a blink detection, which was absent in existing systems. Our results shows that our method achieves better performance compared to existing studies.
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@inproceedings{
10.2312:egve.20241367
,
booktitle = {
ICAT-EGVE 2024 - International Conference on Artificial Reality and Telexistence and Eurographics Symposium on Virtual Environments
},
editor = {
Hasegawa, Shoichi
 and 
Sakata, Nobuchika
 and 
Sundstedt, Veronica
},
title = {{
Learning-based Event-based Human Gaze Tracking with Blink Detection
}},
author = {
Kanno, Mao
 and 
Isogawa, Mariko
},
year = {
2024
},
publisher = {
The Eurographics Association
},
ISSN = {
1727-530X
},
ISBN = {
978-3-03868-245-5
},
DOI = {
10.2312/egve.20241367
}
}
Citation
URI
https://doi.org/10.2312/egve.20241367
 https://diglib.eg.org/handle/10.2312/egve20241367
Collections
ICAT-EGVE2024