@inproceedings{390398b06e404dc5b25c362eaef315c4,
title = "Yawning detection by the analysis of variational descriptor for monitoring driver drowsiness",
abstract = "The road safety is a problem which was approached by several countries following a big raise of the number of accidents. The drowsiness represents one among the causes of the road accidents. The accidents related to the drowsiness often occur on the highways, but also on the main roads, even inside the localities. Today, it is possible to detect the state of tiredness of the driver with the development of the technology of the computer vision. The results of research in physiology show that the first level of lack of vigilance appears by an increase in the frequency of the yawn. In this work, we propose a novel approach for yawning detection for monitoring driver fatigue. In fact, our approach rests on the study of the spatio-Temporal descriptors of a nonstationary and non-linear signal. This approach is evaluated by both YawDD [1] and our MiraclHB [2] databases. The evaluation shows many promising results and shows the effectiveness of the suggested approach.",
keywords = "Driver drowsiness detection, Human safety, Intelligent vehicle, Yawning detection",
author = "Belhassen Akrout and Walid Mahdi",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2nd International Image Processing, Applications and Systems Conference, IPAS 2016 ; Conference date: 05-11-2016 Through 07-11-2016",
year = "2017",
month = mar,
day = "16",
doi = "10.1109/IPAS.2016.7880127",
language = "English",
series = "IPAS 2016 - 2nd International Image Processing, Applications and Systems Conference",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "IPAS 2016 - 2nd International Image Processing, Applications and Systems Conference",
address = "United States",
}
