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A Driver Gaze Estimation Method Based on Deep Learning.

Sayyed Mudassar Shah1, Zhaoyun Sun1, Khalid Zaman1

  • 1Information Engineering School, Chang'an University, Xi'an 710061, China.

Sensors (Basel, Switzerland)
|May 28, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel advanced driver-assistance technique (ADAT) using gaze tracking to monitor driver attention. This system enhances road safety by detecting distractions and notifying drivers of potential dangers.

Keywords:
CNNInception-v3InceptionResNet-v2You Only Look Once (YOLO)advanced driver-assistance technique

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Automotive Safety

Background:

  • Road accidents are a leading cause of death, often due to driver distraction.
  • Advanced Driver-Assistance Techniques (ADAT) can mitigate crashes by alerting drivers to hazards.

Purpose of the Study:

  • To develop an efficient ADAT leveraging driver attention through gaze tracking.
  • To create a real-time system for monitoring driver gaze and head pose to predict and prevent accidents.

Main Methods:

  • Developed a benchmark dataset for driver head poses and eye gaze directions.
  • Utilized a modified YOLO-V4 face detector with Inception-v3 for robust face detection.
  • Employed transfer learning with InceptionResNet-v2 CNN, incorporating a regression layer for head pose and gaze estimation.

Main Results:

  • Achieved 91% average accuracy for head pose detection.
  • Estimated vertical eye gaze with a Root Mean Square Error (RMSE) of 2.68.
  • Estimated horizontal eye gaze with an RMSE of 3.61.

Conclusions:

  • The proposed gaze tracking system accurately estimates head pose and eye gaze directions.
  • This technology can significantly enhance ADAT systems for improved road safety.