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

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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.
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Dates and versions

hal-01837080 , version 1 (12-07-2018)

Identifiers

  • HAL Id : hal-01837080 , version 1

Cite

Miguel García-García, Alice Caplier, Michèle Rombaut. Sleep Deprivation Detection for Real-Time Driver Monitoring using Deep Learning. ICIAR 2018 - 15th International Conference on Image Analysis and Recognition, Jun 2018, Povoa de Varzim, Portugal. ⟨hal-01837080⟩
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