Multi-Feature Fuzzy Inference Method for Fatigue Driving Detection Based on Facial Key Points

Fatigued driving is a major cause of traffic accidents. Making accurate and effective fatigue driving detection has significant implications for road safety. This paper proposes a multi-feature fuzzy inference method for fatigue driving based on facial key points. Firstly, the supervised descent method is introduced into fatigue detection to get a more accurate location of facial key points. Secondly, according to the location of the two-dimensional facial key points, three-dimensional head model, and camera internal parameters, pitch angle that characterize the head pose is calculated iteratively. Finally, a multi-feature fuzzy inference method is adopted to judge the driver state based on the eye-blink, mouth-yawn, and head-tilt. Videos from the YawDD dataset and videos taken by ourselves are used to verify the algorithm of fatigue detection. The experimental results show that the average accuracy is 91.6%. The method is transplanted into the Samsung Exynos 4412 embedded development board. After some acceleration strategies applied, the system proposed in this paper meets the real-time requirements.

Language

  • English

Media Info

  • Media Type: Web
  • Pagination: pp 4314-4326
  • Monograph Title: CICTP 2020: Transportation Evolution Impacting Future Mobility

Subject/Index Terms

Filing Info

  • Accession Number: 01768451
  • Record Type: Publication
  • ISBN: 9780784483053
  • Files: TRIS, ASCE
  • Created Date: Dec 9 2020 3:06PM