A Hybrid Gaze Distance Estimation via Cross-Reference of Vergence and Depth
To deliver an optimal Mixed Reality (MR) experience, wherein virtual elements and real-world objects are seamlessly merged, it is vital to ensure a consistent vergence-accommodation distance. This necessitates the advancement of technology to precisely estimate the user’s gaze distance. P...
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10772443/ |
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| author | Dae-Yong Cho Min-Koo Kang |
| author_facet | Dae-Yong Cho Min-Koo Kang |
| author_sort | Dae-Yong Cho |
| collection | DOAJ |
| description | To deliver an optimal Mixed Reality (MR) experience, wherein virtual elements and real-world objects are seamlessly merged, it is vital to ensure a consistent vergence-accommodation distance. This necessitates the advancement of technology to precisely estimate the user’s gaze distance. Presently, various MR devices employ small eye-tracking cameras to capture both eyes and infer the gaze distance based on vergence angle data. However, this technique faces significant challenges, as it is highly sensitive to several human errors, such as strabismus, blinking, and fatigue of the eyes due to prolonged use. To address these issues, this paper introduces an innovative hybrid algorithm for estimating gaze distances. The proposed approach concurrently utilizes an eye camera and a depth camera to conduct parallel estimations: one based on the conventional vergence angle and the other on gaze-mapped depth information. The confidence of each method is then assessed and cross-referenced, and an adaptive weighted average is computed to derive a more precise and stable gaze distance estimation. In the experiment, three challenging test scenarios designed to induce human and environmental errors were administered to 12 subjects under uniform conditions to evaluate the accuracy and stability of the proposed method. The experimental results were validated through both qualitative and quantitative analysis. The findings showed that the proposed method significantly outperformed current methods with a visual angle error of 0.132 degrees under ideal conditions. Furthermore, it consistently maintained robustness against human and environmental errors, achieving an error range of 0.14 to 0.21 degrees even in demanding environments. |
| format | Article |
| id | doaj-art-5de08eee76644786bccf5121c46fd7cf |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-5de08eee76644786bccf5121c46fd7cf2025-08-20T01:54:38ZengIEEEIEEE Access2169-35362024-01-011218261818262610.1109/ACCESS.2024.351035710772443A Hybrid Gaze Distance Estimation via Cross-Reference of Vergence and DepthDae-Yong Cho0https://orcid.org/0000-0002-6685-5306Min-Koo Kang1https://orcid.org/0000-0003-1109-4818Korea Institute of Science and Technology, Seoul, South KoreaKorea Institute of Science and Technology, Seoul, South KoreaTo deliver an optimal Mixed Reality (MR) experience, wherein virtual elements and real-world objects are seamlessly merged, it is vital to ensure a consistent vergence-accommodation distance. This necessitates the advancement of technology to precisely estimate the user’s gaze distance. Presently, various MR devices employ small eye-tracking cameras to capture both eyes and infer the gaze distance based on vergence angle data. However, this technique faces significant challenges, as it is highly sensitive to several human errors, such as strabismus, blinking, and fatigue of the eyes due to prolonged use. To address these issues, this paper introduces an innovative hybrid algorithm for estimating gaze distances. The proposed approach concurrently utilizes an eye camera and a depth camera to conduct parallel estimations: one based on the conventional vergence angle and the other on gaze-mapped depth information. The confidence of each method is then assessed and cross-referenced, and an adaptive weighted average is computed to derive a more precise and stable gaze distance estimation. In the experiment, three challenging test scenarios designed to induce human and environmental errors were administered to 12 subjects under uniform conditions to evaluate the accuracy and stability of the proposed method. The experimental results were validated through both qualitative and quantitative analysis. The findings showed that the proposed method significantly outperformed current methods with a visual angle error of 0.132 degrees under ideal conditions. Furthermore, it consistently maintained robustness against human and environmental errors, achieving an error range of 0.14 to 0.21 degrees even in demanding environments.https://ieeexplore.ieee.org/document/10772443/Augmented reality (AR)extended reality (XR)eye trackinggaze distance estimationmixed reality (MR)varifocal |
| spellingShingle | Dae-Yong Cho Min-Koo Kang A Hybrid Gaze Distance Estimation via Cross-Reference of Vergence and Depth IEEE Access Augmented reality (AR) extended reality (XR) eye tracking gaze distance estimation mixed reality (MR) varifocal |
| title | A Hybrid Gaze Distance Estimation via Cross-Reference of Vergence and Depth |
| title_full | A Hybrid Gaze Distance Estimation via Cross-Reference of Vergence and Depth |
| title_fullStr | A Hybrid Gaze Distance Estimation via Cross-Reference of Vergence and Depth |
| title_full_unstemmed | A Hybrid Gaze Distance Estimation via Cross-Reference of Vergence and Depth |
| title_short | A Hybrid Gaze Distance Estimation via Cross-Reference of Vergence and Depth |
| title_sort | hybrid gaze distance estimation via cross reference of vergence and depth |
| topic | Augmented reality (AR) extended reality (XR) eye tracking gaze distance estimation mixed reality (MR) varifocal |
| url | https://ieeexplore.ieee.org/document/10772443/ |
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