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Gaze Zone Classification for Driving Studies Using YOLOv8 Image Classification.

Frouke Hermens1, Wim Anker1, Charmaine Noten1

  • 1Department of Computer Science, Open University of the Netherlands, 6419 AT Heerlen, The Netherlands.

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Summary
This summary is machine-generated.

This study introduces an accurate, automated system for gaze zone detection in drivers using YOLOv8 image classification. The method requires no image pre-processing and achieves high accuracy when models are trained for specific drivers and conditions.

Keywords:
YOLOv8driversgaze zoneimage classification

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Area of Science:

  • Computer Vision
  • Road Safety
  • Human-Computer Interaction

Background:

  • Gaze zone detection is crucial for road safety research, identifying driver attention areas.
  • Existing methods for automatic gaze zone annotation can be complex and require pre-processing.
  • Developing an accurate, user-friendly system is essential for practical application.

Purpose of the Study:

  • To develop and validate an automated gaze zone detection system using YOLOv8 for road safety research.
  • To assess the accuracy of YOLOv8 for gaze zone classification across different datasets and conditions.
  • To provide user-friendly applications for data collection and model training.

Main Methods:

  • Utilized YOLOv8 for image classification of driver gaze zones.
  • Tested the system on existing and newly collected datasets with varying numbers of gaze zones (9, 10, and 12).
  • Trained YOLOv8 models specifically for driver demographics and conditions (e.g., glasses, sunglasses).

Main Results:

  • Achieved near-perfect accuracy in gaze zone detection without image pre-processing.
  • Demonstrated high performance when YOLOv8 models were tailored to specific drivers and driving conditions.
  • Developed companion apps for image collection and YOLOv8 model training/application.

Conclusions:

  • YOLOv8 offers a highly accurate and efficient solution for automated gaze zone detection in road safety.
  • The system's accuracy is dependent on training data reflecting specific drivers and conditions.
  • Further research is needed to evaluate performance in diverse, real-world driving scenarios.