Show simple item record

AuthorJabbar, Rateb
AuthorShinoy, Mohammed
AuthorKharbeche, Mohamed
AuthorAl-Khalifa, Khalifa
AuthorKrichen, Moez
AuthorBarkaoui, Kamel
Available date2023-10-16T06:59:51Z
Publication Date2020-02-01
Publication Name2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies, ICIoT 2020
Identifierhttp://dx.doi.org/10.1109/ICIoT48696.2020.9089484
CitationJabbar, R., Shinoy, M., Kharbeche, M., Al-Khalifa, K., Krichen, M., & Barkaoui, K. (2020, February). Driver drowsiness detection model using convolutional neural networks techniques for android application. In 2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies (ICIoT) (pp. 237-242). IEEE.‏
ISBN9781728148212
URIhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85085482136&origin=inward
URIhttp://hdl.handle.net/10576/48528
AbstractA sleepy driver is arguably much more dangerous on the road than the one who is speeding as he is a victim of microsleeps. Automotive researchers and manufacturers are trying to curb this problem with several technological solutions that will avert such a crisis. This article focuses on the detection of such micro sleep and drowsiness using neural network-based methodologies. Our previous work in this field involved using machine learning with multi-layer perceptron to detect the same. In this paper, accuracy was increased by utilizing facial landmarks which are detected by the camera and that is passed to a Convolutional Neural Network (CNN) to classify drowsiness. The achievement with this work is the capability to provide a lightweight alternative to heavier classification models with more than 88% for the category without glasses, more than 85% for the category night without glasses. On average, more than 83% of accuracy was achieved in all categories. Moreover, as for model size, complexity and storage, there is a marked reduction in the new proposed model in comparison to the benchmark model where the maximum size is 75 KB. The proposed CNN based model can be used to build a real-time driver drowsiness detection system for embedded systems and Android devices with high accuracy and ease of use.
SponsorThis publication was made possible by an NPRP award [NPRP8-910-2-387] from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
SubjectAndroid
Convolutional Neural Networks
Driver Behaviour Monitoring System
Drowsiness Detection
Facial Landmarks
Real-Time Deep Learning
TitleDriver Drowsiness Detection Model Using Convolutional Neural Networks Techniques for Android Application
TypeConference Paper
Pagination237-242
dc.accessType Open Access


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record