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An Integrated Framework for Multi-State Driver Monitoring Using Heterogeneous Loss and Attention-Based Feature

Zhongxu Hu1, Yiran Zhang1, Yang Xing2

  • 1School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798, Singapore.

Sensors (Basel, Switzerland)
|October 14, 2022
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Summary
This summary is machine-generated.

This study introduces an advanced visual driver monitoring system for intelligent vehicles, accurately tracking head pose, gaze, blinking, and yawning. The novel framework achieves state-of-the-art performance in driver state estimation.

Keywords:
cascade cross-entropydriver statefeature decouplinggaze consistency

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Area of Science:

  • Intelligent Transportation Systems
  • Computer Vision
  • Human-Computer Interaction

Background:

  • Multi-state driver monitoring is crucial for developing human-centric intelligent driving systems.
  • Accurate estimation of driver states like head pose and gaze is challenging.
  • Existing methods often lack integration and robustness in real-world scenarios.

Purpose of the Study:

  • To propose an integrated visual-based multi-state driver monitoring framework.
  • To enhance the accuracy and robustness of driver state estimation (head rotation, gaze, blinking, yawning).
  • To address challenges in head pose and gaze estimation through a unified network architecture.

Main Methods:

  • Developed a unified network for head pose and gaze estimation treated as soft classification tasks.
  • Introduced a feature decoupling module to separate features across different axis domains.
  • Designed a cascade cross-entropy loss function and incorporated gaze consistency for optimized estimation.

Main Results:

  • The proposed framework integrates head rotation, gaze, blinking, and yawning detection.
  • Achieved state-of-the-art performance on widely used benchmark datasets.
  • Experimental results demonstrate superior accuracy and robustness compared to existing methods.

Conclusions:

  • The integrated visual driver monitoring framework significantly advances intelligent driving systems.
  • The proposed unified network architecture and loss functions effectively improve driver state estimation.
  • This research provides a robust solution for real-time, multi-state driver monitoring.