Citation

Flores-Monroy J, Nakano-Miyatake M, Perez-Meana H, Escamilla-Hernandez E, Sanchez-Perez G, Vergara-Villegas OO, Cruz-Sánchez VG, Sossa-Azuela JH, Carrasco-Ochoa JA, Martínez-Trinidad JF, Olvera-López JA. Lect. Notes Comput. Sci. 2022; 13264: 83-93.

Copyright

(Copyright © 2022, Holtzbrinck Springer Nature Publishing Group)

DOI

10.1007/978-3-031-07750-0_8

PMID

unavailable

Abstract

The driver's drowsiness and distraction are the principal causes of traffic accidents in the world. To attack this problem, in this paper we propose a visual-based driver's drowsiness and distraction detection system, which is based on a face detection algorithm and a CNN-based driver state classification. To be useful the proposed system, we consider that the system must be implemented in a compact mobile device with limited memory space and computational power. The proposed system in compact mobile device can be used in any type of vehicle, avoiding accident caused by lack of driver's alert. The proposed system is evaluated using public dataset, obtaining 95.77% of global accuracy. The proposed system is compared with five finetuned off-the-shelf CNNs, in which the proposed system shows a favorable performance, providing higher operation speed and lower memory requirement compared with these five CNNs, although the detection accuracy is slightly lower compared with the best CNN. The performance of the proposed system guarantees the real-time operation in the compact mobile device.

Language: en

Keywords

Convolutional Neural Networks (CNN); Driver’s distraction detection; Driver’s drowsiness detection; Finetuning model; Real-time implementation