Eye Tracking Accuracy via Pupil Shape Modeling
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Solution Overview
Problem
Current eye tracking methods in VR/AR systems face challenges in accurately determining gazing vectors and head center positions, leading to reduced accuracy and user experience in virtual reality environments.
Innovation Solution
An eye tracking method and electronic device that constructs an eye model using pupil shape information, captures images of the eye, and adjusts the head center position based on actual and simulated pupil shape information to improve gazing vector determination and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional eye tracking methods are used in VR/AR systems, then the system can provide basic eye tracking functionality, but the accuracy of gazing vector determination and head center position is reduced
Solution Approach 1:
The system performs preliminary calibration by capturing multiple images of the user's eye at different known gazing positions before actual eye tracking begins. These pre-captured images are used to establish the relationship between pupil shape variations and gazing vectors, creating a calibration dataset that improves subsequent measurement accuracy.
Solution Approach 2:
The system creates a digital copy or model of the user's specific eye geometry based on calibration images. This eye model includes parameters such as corneal curvature, pupil size, and iris patterns, which are then used to simulate and compare against real-time pupil shapes, enabling more accurate gazing vector determination without requiring complex physical measurements.
2Measurement precision
If multiple pupil shape information data are collected and analyzed, then the accuracy of eye tracking is improved, but the computational complexity and processing time increase
Solution Approach 1:
The system extracts only the most relevant features from captured eye images, such as pupil center position, pupil radius, and iris boundary points, rather than processing entire image datasets. This feature extraction approach maintains measurement precision while significantly reducing the computational burden on the processing circuit.
Solution Approach 2:
The system transforms raw image data into simplified geometric parameters (e.g., converting pixel coordinates to angular measurements, representing pupil shapes as ellipses with specific parameters). This parameter transformation reduces data dimensionality and complexity while preserving the essential information needed for accurate eye tracking calculations.
Data Source
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AI summary
An eye tracking method includes: constructing, by a processing circuit, an eye model; analyzing, by the processing circuit, a first head center position, according to a plurality of first pupil shape information and the eye model, wherein the plurality of first pupil shape information correspond to a plurality of first gazing vectors; capturing, by a camera circuit, a first image of the eye; analyzing, by the processing circuit, a determined gazing vector, according to the eye model and the first image; and adjusting, by the processing circuit, the first head center position according to an actual pupil shape information group and a plurality of simulated pupil shape information groups.