Group Eye Movement Trajectory Fitting for AOI Fixation Sequence Analysis
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Solution Overview
Problem
Existing methods for eye movement analysis fail to automatically identify and reflect changes in fixation sequence among different areas of interest during human-machine interaction, limiting the ability to study group eye movement characteristics.
Innovation Solution
A method and apparatus for generating a group eye movement trajectory by acquiring individual eye movement data, filtering and fitting similar trajectories to generate a representative group trajectory, and displaying it in real time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If individual eye movement trajectories are collected from multiple users, then the data quantity for analysis is improved, but the complexity of processing and generating group-level trajectories increases
Solution Approach 1:
The patent merges multiple individual eye movement trajectories into a single group eye movement trajectory by identifying common fixation sequences across users. The system combines individual trajectory data (fixation points, areas of interest, and sequences) to generate a representative group trajectory that reflects collective visual behavior patterns.
Solution Approach 2:
The patent creates a representative group eye movement trajectory that copies and generalizes the common patterns found in individual trajectories. By identifying recurring fixation sequences across multiple users, the system generates a standardized group trajectory that represents the collective visual attention patterns without requiring manual aggregation of all individual data points.
2Measurement precision
If manual division of eye movement characteristics by age groups is performed, then the analysis depth is improved, but the automation level and efficiency deteriorate
Solution Approach 1:
The patent enables the system to automatically perform what previously required manual intervention. The group eye movement trajectory generation is achieved through automated algorithms that process individual trajectories, identify common fixation sequences, and generate group-level results without manual division or categorization by age groups or other demographics.
Solution Approach 2:
The patent replaces the manual mechanical process of dividing and analyzing eye movement data by age groups with an automated computational system. The system uses algorithms to process trajectories and identify patterns, substituting human manual analysis with automated image processing and data analysis techniques.
3Device complexity
If fixation sequences among different areas of interest are not automatically identified, then the system simplicity is maintained, but the ability to reflect visual behavior changes deteriorates
Solution Approach 1:
The patent segments the eye movement trajectory into discrete fixation points and areas of interest (AOIs). By dividing the continuous eye movement data into distinct fixation events and mapping them to specific AOIs, the system can automatically identify and track the sequence in which users view different areas, enabling automated analysis of visual behavior patterns.
Solution Approach 2:
The patent introduces areas of interest (AOIs) as intermediary elements that mediate between raw eye movement data and meaningful visual behavior analysis. By defining specific AOIs and tracking which AOIs are fixated and in what sequence, the system automatically captures visual behavior changes without requiring complex direct analysis of continuous trajectory data.
Data Source
AI summary
The present application provides a method and apparatus for generating a group eye movement trajectory, a computing device, and a storage medium, belonging to the technical field of vision tracking. The method includes: acquiring eye movement data of each individual in a target group on a fixation object; generating an individual fixation trajectory of each individual on a plurality of areas of interest in the fixation object based on the eye movement data; and performing similarity fitting on the plurality of individual fixation trajectories to generate the group eye movement trajectory.