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Rizwan Ali Naqvi1, Muhammad Arsalan1, Ganbayar Batchuluun1

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Summary
This summary is machine-generated.

Preventing driver inattention requires advanced gaze classification. This study introduces a deep learning method using near-infrared cameras for accurate driver gaze detection without calibration, improving driving safety.

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NIR camera sensordeep learningdriver attentioneye gaze trackinguser calibration

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Area of Science:

  • Computer Vision
  • Human-Computer Interaction
  • Automotive Safety

Background:

  • Driver inattention is a major cause of automobile accidents.
  • Existing gaze detection methods often rely on head movements, which are insufficient for accurate attention monitoring.
  • Challenges in real-world driving include lighting variations, reflections, and occlusions, complicating gaze detection.

Purpose of the Study:

  • To develop an accurate and inexpensive gaze classification system for drivers.
  • To overcome limitations of head-movement-based and traditional pupil center corneal reflection (PCCR) methods.
  • To propose a deep learning-based gaze detection method that does not require user calibration.

Main Methods:

  • Utilized a deep learning approach with a near-infrared (NIR) camera sensor.
  • The method accounts for both driver head and eye movements.
  • No initial user calibration is required for the system.

Main Results:

  • The proposed deep learning method demonstrated higher accuracy compared to previous gaze classification techniques.
  • Evaluated on a self-constructed database and the CAVE dataset, confirming its effectiveness.
  • Successfully addressed challenges like lighting variations and occlusions inherent in car environments.

Conclusions:

  • The developed deep learning-based gaze detection system offers a more accurate and practical solution for driver attention monitoring.
  • This technology has the potential to significantly enhance automotive safety by reducing accidents caused by inattention.
  • The system's ability to function without calibration makes it suitable for real-world driving conditions.