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Sleep Deprivation Detection for Real-Time Driver Monitoring Using Deep Learning

  • Miguel García-GarcíaEmail author
  • Alice Caplier
  • Michele Rombaut
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10882)

Abstract

We propose a non-invasive method to detect sleep deprivation by evaluating a short video sequence of a subject. Computer Vision techniques are used to crop the face from every frame and classify it (within a Deep Learning framework) into two classes: “rested” or “sleep deprived”. The system has been trained on a database of subjects recorded under severe sleep deprivation conditions. A prototype has been implemented in a low-cost Android device proving its viability for real-time driver monitoring applications. Tests on real world data have been carried out and show encouraging performances but also reveal the need of larger datasets for training.

Keywords

Mobilenet Road safety Driver drowsiness Sleep deprivation 

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Innov+, Batiment 503, Centre Universitaire d’OrsayOrsayFrance
  2. 2.Univ. Grenoble Alpes, CNRS, Grenoble INP, GIPSA-labGrenobleFrance

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