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Automatic Visual Attention Detection for Mobile Eye Tracking Using Pre-Trained Computer Vision Models and Human Gaze.

Michael Barz1,2, Daniel Sonntag1,2

  • 1German Research Center for Artificial Intelligence (DFKI), Interactive Machine Learning Department, Stuhlsatzenhausweg 3, Saarland Informatics Campus D3_2, 66123 Saarbrücken, Germany.

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Summary
This summary is machine-generated.

This study introduces automated methods for detecting visual attention to areas of interest (AOIs) in eye-tracking data. These deep learning approaches aim to accelerate research by reducing manual annotation, especially for mobile eye-tracking applications.

Keywords:
area of interestcomputer visioneye trackingeye tracking data analysisvisual attention

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Area of Science:

  • Computer Vision
  • Cognitive Science
  • Human-Computer Interaction

Background:

  • Visual attention processing is crucial for decision-making.
  • Eye-tracking studies often use manual annotation of Areas of Interest (AOIs).
  • Manual annotation is time-consuming and subjective, hindering research scalability.

Purpose of the Study:

  • To develop and evaluate automated methods for detecting visual attention to AOIs.
  • To accelerate and objectify eye-tracking research, particularly for mobile and egocentric data.
  • To establish an evaluation framework for assessing automatic visual attention detection.

Main Methods:

  • Implementation of two deep learning methods (image classification, object detection) for AOI attention detection.
  • Utilizing pre-trained deep learning models for efficient processing.
  • Development of an evaluation framework using the VISUS dataset and activity recognition metrics.

Main Results:

  • Systematic evaluation of the implemented methods within the developed framework.
  • Analysis of the potentials and limitations of automatic visual attention detection.
  • Identification of areas for improvement in future automated systems.

Conclusions:

  • Automated visual attention detection shows promise for accelerating eye-tracking research.
  • Deep learning offers a viable approach for objective AOI annotation.
  • Further research can enhance the performance and applicability of these methods.