Adaptive Eye-Tracking Calibration for Displacement Compensation
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Solution Overview
Problem
Conventional eye-tracking devices suffer from inaccuracy and error due to displacement or sliding during operation, leading to calibration issues and reduced usage time due to continuous power requirements.
Innovation Solution
An adaptive eye-tracking calibration method that generates a current eye model, compares real-time pupil data with a predetermined threshold, and automatically updates or recalibrates the model when significant differences are detected, allowing for continuous accurate gaze position tracking without user intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional eye-tracking calibration is performed, then initial calibration accuracy is achieved, but the device suffers from inaccuracy and error due to displacement during operation
Solution Approach 1:
The system performs preliminary calibration to establish an initial eye model, then proactively detects displacement by comparing real-time pupil data with the current eye model before significant accuracy degradation occurs. This allows the system to recalibrate adaptively, maintaining measurement precision despite device displacement during operation.
2Duration of action of moving object
If continuous power is supplied for tracking, then continuous gaze monitoring is achieved, but usage time is limited to a couple of hours
Solution Approach 1:
Instead of continuous recalibration, the system implements periodic adaptive recalibration by detecting displacement events through pupil data comparison. The calibration process is triggered only when displacement is detected (when pupil data difference exceeds threshold), converting continuous power consumption into event-driven periodic action, thereby extending usage time while maintaining accuracy.
3Measurement precision
If frequent recalibration is performed to maintain accuracy, then gaze position accuracy is maintained, but power consumption increases and usage time decreases
Solution Approach 1:
The system continuously monitors pupil data and compares it with the current eye model to detect displacement events. This feedback mechanism triggers recalibration only when necessary (when pupil data difference exceeds a predetermined threshold), avoiding unnecessary recalibrations and extending usage time while maintaining gaze position accuracy through adaptive, condition-based recalibration.
4Measurement precision
If manual recalibration is required after displacement, then accuracy can be restored, but user intervention is needed and productivity decreases
Solution Approach 1:
The system automatically detects device displacement by comparing real-time pupil data with the stored eye model and autonomously triggers recalibration when displacement is detected. This self-service capability eliminates the need for user intervention, maintaining gaze position accuracy and ensuring operation continuity without impacting productivity.
Data Source
AI summary
An adaptive eye-tracking calibration method includes generating an eye model acting as a current eye model; calibrating the eye model; comparing real-time pupil data set and pupil data set of the current eye model to obtain a pupil data difference when an event happens; and generating a new eye model if the pupil data difference is greater than a predetermined threshold.