Drowsiness Classification for Internal Driving Situation Awareness on Mobile Platform

Abstract: the sleeping driver is potentially more likely to cause an accident than the person who speeds up since the driver is the victim of sleepiness. Automobile industry researchers, including manufacturers, seek to solve this issue with various technical solutions that can avoid such a situation. This paper proposes an implementation of a lightweight method to detect driver's sleepiness using facial landmarks and head pose estimation based on neural network methodologies on a mobile device. We try to improve the accurateness by using face images that the camera detects and passes to CNN to identify sleepiness. Firstly, applied a behavioral landmark's sleepiness detection process. Then, an integrated Head Pose Estimation technique will strengthen the system's reliability. The preliminary findings of the tests demonstrate that with real-time capability, more than 86% identification accuracy can be reached in several real-world scenarios for all classes, including with glasses, without gla.... https://www.bibliothek.tu-chemnitz.de/ojs/index.php/cs/article/view/491

Object Details

• Location: Deutsche Nationalbibliothek Frankfurt am Main
• Extent: Online-Ressource
• Language: Englisch

References and Relationships

• Bibliographic citation: Drowsiness Classification for Internal Driving Situation Awareness on Mobile Platform ; volume:8 ; number:2 ; day:21 ; month:12 ; year:2021 ; Embedded selforganising systems ; 8, Heft 2 (21.12.2021)

Contributors, Places and Time

• Creator: Nine, Julkar ; Ahmed, Naeem ; Mathavan, Rahul

Further information

• DOI: 10.14464/ess.v8i2.491
• URN: urn:nbn:de:101:1-2023032815450336475412
• Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Last update: 14.08.2025, 10:47 AM CEST