Gaze Estimation via Common Coordinate System Calibration
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Solution Overview
Problem
Existing gaze estimation methods require cumbersome calibration procedures, leading to relatively inaccurate estimations, especially when passively tracking user attention in scenes like shopping windows, as they rely on individual-specific or non-scene-specific statistical data.
Innovation Solution
A system and method that calibrate un-calibrated eye measurement points from different users viewing the same scene by mapping them to a common coordinate system, utilizing the insight that gaze patterns between users are similar for the same scene, thereby establishing a more accurate scene transformation for estimating gaze points without individual calibration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If calibration procedures are performed to improve gaze estimation accuracy, then measurement precision improves, but device complexity and time consumption increase
Solution Approach 1:
The system performs automatic self-calibration by capturing natural eye movement data during normal viewing and using statistical analysis to determine calibration parameters without requiring active user participation or manual adjustment procedures
Solution Approach 2:
The system changes the approach from requiring users to fixate on specific calibration points to using statistical distribution of natural eye movement measurements to derive calibration parameters, fundamentally altering the calibration process parameters
2Measurement precision
If individual-specific calibration data is used to improve gaze estimation accuracy, then measurement precision improves, but loss of time increases due to per-user calibration requirements
Solution Approach 1:
The system collects and analyzes eye movement data from multiple users in advance to establish statistical models and calibration parameters that can be applied to new users without requiring them to undergo time-consuming calibration procedures
Solution Approach 2:
The system creates universal calibration models based on statistical analysis of eye movement patterns across multiple users, making the calibration data applicable to multiple users rather than being strictly individual-specific
3Ease of operation
If passive gaze tracking is implemented to reduce user participation, then ease of operation improves, but measurement precision deteriorates
Solution Approach 1:
The system uses statistical feedback from aggregated eye movement data of multiple users to continuously refine and improve the accuracy of passive gaze estimation, using the collected data to adjust and optimize calibration parameters
Data Source
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AI summary
The invention enables estimating gaze from a set of eye measurement points which are indicative of a gaze pattern of a user viewing a scene. Herein, use is made of the insight that gaze patterns of different users are similar for a same scene (010). The invention involves obtaining different sets of eye measurement points from different users viewing the same scene. The different sets of eye measurement points may be mapped to a common coordinate system, and there by mutually calibrated, based on the assumption that geometrically similar topologies between the different sets of eye measurement points are to be mapped to similar coordinates in the common coordinate system as they represent a similar or same gaze pattern.A scene transformation for mapping the common coordinate system to a coordinate system associated with the scene can be calculated by matching eye measurement points from the common coordinate system to interest points(012) of the scene. The scene transformation is thereby calculated more accurately than individually calculated scene transformations, thereby providing a more accurate estimate of the gaze points.