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Driver Drowsiness Detection

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Computer Vision for Driver Assistance

Part of the book series: Computational Imaging and Vision ((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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Authors and Affiliations

  1. Department of Computer Engineering, Qazvin Islamic Azad University, Qazvin, Iran

    Mahdi Rezaei

  2. Department of Electrical and Electronic Engineering, Auckland University of Technology, Auckland, New Zealand

    Reinhard Klette

Authors
  1. Mahdi Rezaei
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  2. Reinhard Klette
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Rezaei, M., Klette, R. (2017). Driver Drowsiness Detection. In: Computer Vision for Driver Assistance. Computational Imaging and Vision, vol 45. Springer, Cham. https://doi.org/10.1007/978-3-319-50551-0_5

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  • DOI: https://doi.org/10.1007/978-3-319-50551-0_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-50549-7

  • Online ISBN: 978-3-319-50551-0

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