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Short-time AOIs-based representative scanpath identification and scanpath aggregation.

He Huang¹, Philipp Doebler², Barbara Mertins^2,3

¹Department of Statistics, TU Dortmund University, 44227, Dortmund, Germany. he.huang@tu-dortmund.de.

Behavior Research Methods

|January 9, 2024

Summary

This summary is machine-generated.

A novel algorithm identifies representative scanpaths using short-time areas of interest (AOIs), outperforming existing methods in eye-tracking analysis for diverse visual stimuli.

Keywords:

Aggregation Eye-tracking Representative scanpath Scanpath clustering Scanpaths

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Area of Science:

Cognitive Science
Computer Vision
Human-Computer Interaction

Background:

Scanpath analysis in eye-tracking research aims to identify representative viewing patterns.
Existing methods often rely on predefined areas of interest (AOIs) and can be limited by scanpath similarity selection.

Purpose of the Study:

To develop and evaluate a new algorithm for identifying a representative scanpath in eye-tracking data.
To introduce the concept of short-time areas of interest (AOIs) for improved scanpath analysis.

Main Methods:

The proposed algorithm identifies representative scanpaths by extracting fixations from dynamically defined short-time areas of interest (AOIs).
The method was evaluated using eye-tracking data from two publicly available datasets.

Main Results:

The new algorithm demonstrated superior performance compared to existing methods on the evaluated datasets.
The approach effectively handles both static and dynamic visual stimuli, including those without natural segmentation.

Conclusions:

The proposed algorithm offers a robust solution for representative scanpath identification in eye-tracking studies.
This method addresses limitations associated with traditional AOI selection and scanpath similarity measures, enhancing the analysis of visual attention.