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

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dc.contributor.authorKanno, Maoen_US
dc.contributor.authorIsogawa, Marikoen_US
dc.contributor.editorHasegawa, Shoichien_US
dc.contributor.editorSakata, Nobuchikaen_US
dc.contributor.editorSundstedt, Veronicaen_US
dc.date.accessioned2024-11-29T06:43:08Z
dc.date.available2024-11-29T06:43:08Z
dc.date.issued2024
dc.description.abstractThis 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.en_US
dc.description.sectionheadersRendering and Sensing
dc.description.seriesinformationICAT-EGVE 2024 - International Conference on Artificial Reality and Telexistence and Eurographics Symposium on Virtual Environments
dc.identifier.doi10.2312/egve.20241367
dc.identifier.isbn978-3-03868-245-5
dc.identifier.issn1727-530X
dc.identifier.pages5 pages
dc.identifier.urihttps://doi.org/10.2312/egve.20241367
dc.identifier.urihttps://diglib.eg.org/handle/10.2312/egve20241367
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleLearning-based Event-based Human Gaze Tracking with Blink Detectionen_US
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