Open Your Eyes: Eyelid Aperture Estimation in Driver Monitoring Systems
Authors: Luis Salgado
Date: 17.10.2021
Abstract
Driver Monitoring Systems (DMS) operate by measuring the state of the driver while performing driving activities. At the gates of the arrival of SAE-L3 autonomous driving vehicles, DMS are called to play a major role for guarantee or, at least, support safer mode transfer transitions (between manual and automated driving modes). Drowsiness and fatigue detection with cameras is still one of the major targets of DMS research and investment. In this work we present our eyelid aperture estimation method, as enabling method for estimating such physiological status, in the context of two main use cases. First, we show how the technique can be integrated into a DMS system, along with other outside-sensing components, to showcase SAE-L3 demonstrations. Second, we adopt the DMD (Driver Monitoring Dataset) open dataset project with a twofold purpose: evaluate the quality of our method compare to other state-of-the-art techniques, and to contribute to the DMD with ground truth labels about drowsiness concepts.
BIB_text
title = {Open Your Eyes: Eyelid Aperture Estimation in Driver Monitoring Systems},
pages = {165-189},
keywds = {
Eyelid aperture, Blink detection, Drowsiness, Driver monitoring
}
abstract = {
Driver Monitoring Systems (DMS) operate by measuring the state of the driver while performing driving activities. At the gates of the arrival of SAE-L3 autonomous driving vehicles, DMS are called to play a major role for guarantee or, at least, support safer mode transfer transitions (between manual and automated driving modes). Drowsiness and fatigue detection with cameras is still one of the major targets of DMS research and investment. In this work we present our eyelid aperture estimation method, as enabling method for estimating such physiological status, in the context of two main use cases. First, we show how the technique can be integrated into a DMS system, along with other outside-sensing components, to showcase SAE-L3 demonstrations. Second, we adopt the DMD (Driver Monitoring Dataset) open dataset project with a twofold purpose: evaluate the quality of our method compare to other state-of-the-art techniques, and to contribute to the DMD with ground truth labels about drowsiness concepts.
}
isbn = {978-3-030-89169-5},
date = {2021-10-17},
}
