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Related Experiment Video

Updated: Jan 10, 2026

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Low-Cost Eye-Tracking Fixation Analysis for Driver Monitoring Systems Using Kalman Filtering and OPTICS Clustering.

Jonas Brandstetter1,2, Eva-Maria Knoch1, Frank Gauterin1

  • 1Faculty of Mechanical Engineering, Institute for Vehicle Systems Engineering, Karlsruhe Institute of Technology (KIT), 76131 Karlsruhe, Germany.

Sensors (Basel, Switzerland)
|November 27, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a software pipeline to extract eye-tracking fixation features from standard RGB video, offering a low-cost alternative for driver monitoring systems. The method enhances fixation stability and reduces jitter without specialized hardware.

Keywords:
Kalman filterOPTICS clusteringdriver monitoringeye trackingfixation analysis

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Area of Science:

  • Computer Vision
  • Human-Computer Interaction
  • Automotive Safety

Background:

  • Driver monitoring systems (DMS) rely on eye-tracking for attention analysis.
  • Existing eye-tracking hardware is expensive and complex for large-scale integration.
  • Need for cost-effective solutions using readily available hardware.

Purpose of the Study:

  • Develop a software pipeline to extract fixation-related eye-tracking features from conventional RGB video.
  • Enable low-cost driver monitoring using standard cameras.
  • Demonstrate the feasibility of software-based eye-tracking for DMS.

Main Methods:

  • Utilized MediaPipe for facial and pupil landmark detection.
  • Applied Kalman filtering for landmark denoising.
  • Employed OPTICS algorithm for fixation center identification within a sliding window.
  • Implemented affine normalization to correct for head motion and camera geometry.
  • Derived fixation segments using smoothed velocity profiles and moving averages.

Main Results:

  • The combined Kalman and OPTICS pipeline significantly reduced landmark jitter.
  • Achieved more stable fixation centroids compared to raw data.
  • Affine normalization enhanced referential pupil stability.
  • The pipeline demonstrated minimal computational overhead.
  • Proof of concept successful in a low-cost RGB setting.

Conclusions:

  • A practical software pipeline can extract valuable fixation features from RGB video for driver monitoring.
  • This approach offers a cost-effective and scalable alternative to dedicated eye-tracking hardware.
  • Future work should address lighting sensitivity and head motion through advanced techniques and broader validation.