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Gaze Focalization System for Driving Applications Using OpenFace 2.0 Toolkit with NARMAX Algorithm in Accidental

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  • 1Electronics Department, University of Alcalá, 28801 Alcalá de Henares, Spain.

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Summary
This summary is machine-generated.

This study introduces a low-cost, non-intrusive system for monitoring driver attention using camera-based gaze estimation. It accurately maps driver focus in real traffic scenes, improving safety for automated driving systems.

Keywords:
NARMAXaccidental scenarioscomputer visiondriver focalizationgaze estimationheat map

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Area of Science:

  • Computer Vision
  • Human-Computer Interaction
  • Automotive Safety

Background:

  • Driver attention monitoring is crucial for advanced driver-assistance systems (ADAS) and autonomous driving.
  • Current gaze estimation techniques are often intrusive and expensive, limiting real-world application.
  • Existing datasets lack real-world accident scenarios, hindering robust system development.

Purpose of the Study:

  • To develop a low-cost, non-intrusive camera-based gaze mapping system for driver attention analysis.
  • To integrate the OpenFace 2.0 Toolkit with NARMAX modeling for accurate gaze estimation.
  • To validate the system's performance on a challenging real-world accident database.

Main Methods:

  • Utilized the OpenFace 2.0 Toolkit for facial landmark extraction and head pose estimation.
  • Employed NARMAX (Nonlinear AutoRegressive Moving Average with eXogenous inputs) modeling to map gaze parameters to screen regions.
  • Developed a heat map visualization of driver focalization.
  • Validated the system using the DADA2000 database, comparing results with a previous linear model and a commercial eye-tracker.

Main Results:

  • The proposed system achieved comparable results to an expensive eye-tracker.
  • Demonstrated accurate visualization of driver focalization in real traffic scenes.
  • Successfully mapped driver attention using low-cost, non-intrusive methods.

Conclusions:

  • The developed system offers a viable, cost-effective solution for driver attention monitoring.
  • This technology can be used to create valuable datasets for training and validating driver monitoring systems.
  • Enhances safety potential for Level 3 and Level 4 automated driving systems by providing reliable gaze estimation.