Driver Drowsiness Detection

  • Mahdi Rezaei
  • Reinhard Klette
Chapter
Part of the Computational Imaging and Vision book series (CIVI, volume 45)

Abstract

In this chapter we propose a method to assess driver drowsiness based on face and eye-status analysis. The chapter starts with a detailed discussion on effective ways to create a strong classifier (the “training phase”), and it continues with a novel optimization method for the “application phase” of the classifier. Both together significantly improve the performance of our Haar-like based detectors in terms of speed, detection rate, and detection accuracy under non-ideal lighting conditions and for noisy images. The proposed framework includes a preprocessing denoising method, introduction of Global Haar-like features, a fast adaptation method to cope with rapid lighting variations, as well as an implementation of a Kalman filter tracker to reduce the search region and to indirectly support our eye-state monitoring system. Experimental results obtained for the MIT-CMU dataset, Yale dataset, and our recorded videos and comparisons with standard Haar-like detectors show noticeable improvements compared to previous methods.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Mahdi Rezaei
    • 1
  • Reinhard Klette
    • 2
  1. 1.Department of Computer EngineeringQazvin Islamic Azad UniversityQazvinIran
  2. 2.Department of Electrical and Electronic EngineeringAuckland University of TechnologyAucklandNew Zealand

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