Focal Depth Estimation: A Calibration-Free, Subject- and Daytime Invariant Approach

In an era where personalized technology is increasingly intertwined with daily life, traditional eye-tracking systems and autofocal glasses face a significant challenge: the need for frequent, user-specific calibration, which impedes their practicality. This study introduces a groundbreaking calibra...

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Bibliographic Details
Main Authors Hosp, Benedikt W, Severitt, Björn, Agarwala, Rajat, Rusak, Evgenia, Sauer, Yannick, Wahl, Siegfried
Format Journal Article
LanguageEnglish
Published 07.08.2024
Subjects
Computer Science - Artificial Intelligence
Computer Science - Computer Vision and Pattern Recognition
Computer Science - Human-Computer Interaction
Computer Science - Learning
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Summary:In an era where personalized technology is increasingly intertwined with daily life, traditional eye-tracking systems and autofocal glasses face a significant challenge: the need for frequent, user-specific calibration, which impedes their practicality. This study introduces a groundbreaking calibration-free method for estimating focal depth, leveraging machine learning techniques to analyze eye movement features within short sequences. Our approach, distinguished by its innovative use of LSTM networks and domain-specific feature engineering, achieves a mean absolute error (MAE) of less than 10 cm, setting a new focal depth estimation accuracy standard. This advancement promises to enhance the usability of autofocal glasses and pave the way for their seamless integration into extended reality environments, marking a significant leap forward in personalized visual technology.
DOI:10.48550/arxiv.2408.03591