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

• URL: http://arxiv.org/abs/2408.03591v1
• Date: Wed, 7 Aug 2024 07:09:14 GMT
• Title: Focal Depth Estimation: A Calibration-Free, Subject- and Daytime Invariant Approach
• Authors: Benedikt W. Hosp, Björn Severitt, Rajat Agarwala, Evgenia Rusak, Yannick Sauer, Siegfried Wahl,
• Abstract summary: This study introduces a groundbreaking calibration-free method for estimating focal depth. We leverage machine learning techniques to analyze eye movement features within short sequences. Our approach achieves a mean absolute error (MAE) of less than 10 cm, setting a new focal depth estimation accuracy standard.
• Score: 0.5026434955540995
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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