TY - GEN
T1 - A CNN-Based Driver’s Drowsiness and Distraction Detection System
AU - Flores-Monroy, Jonathan
AU - Nakano-Miyatake, Mariko
AU - Perez-Meana, Hector
AU - Escamilla-Hernandez, Enrique
AU - Sanchez-Perez, Gabriel
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Convolutional Neural Networks (CNN)
KW - Driver’s distraction detection
KW - Driver’s drowsiness detection
KW - Finetuning model
KW - Real-time implementation
UR - http://www.scopus.com/inward/record.url?scp=85132975418&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-07750-0_8
DO - 10.1007/978-3-031-07750-0_8
M3 - Contribución a la conferencia
AN - SCOPUS:85132975418
SN - 9783031077494
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 83
EP - 93
BT - Pattern Recognition - 14th Mexican Conference, MCPR 2022, Proceedings
A2 - Vergara-Villegas, Osslan Osiris
A2 - Cruz-Sánchez, Vianey Guadalupe
A2 - Sossa-Azuela, Juan Humberto
A2 - Carrasco-Ochoa, Jesús Ariel
A2 - Martínez-Trinidad, José Francisco
A2 - Olvera-López, José Arturo
PB - Springer Science and Business Media Deutschland GmbH
T2 - 14th Mexican Conference on Pattern Recognition, MCPR 2022
Y2 - 22 June 2022 through 25 June 2022
ER -