TY - GEN
T1 - Implementation of a CNN-Based Driver Drowsiness and Distraction Detector in Mobile Devices
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 authors and IOS Press. All rights reserved.
PY - 2022/9/14
Y1 - 2022/9/14
N2 - Drowsiness and driver distraction are considered the main causes of traffic accidents in the world. Considering this situation, this paper proposes two important modifications to our previously proposed driver drowsiness and distraction detector for real-time implementation on handheld mobile devices, such as smartphones. The first modification is due to a large variation in the capacity of mobile devices. To adapt the proposed system to a wide range of mobile devices, we present two automatic threshold calculations, which are used to differentiate driver drowsiness from normal blinking and dangerous driver distraction from normal short-term distraction. The second modification is related to the alarm during a continuous dangerous situation of the driver. We introduce a new algorithm to ensure the continuous activation of the alarm while the dangerous situation continues. These improvements perform as the general algorithm, since when it was implemented in mobile devices with low computational power, as well as in devices that do not have these limitations, the alarm activation times were not affected; On the other hand, it was possible to increase the accuracy originally given by the first system with respect to Ground Truth by almost 25% on average, resulting in alarm activations not being affected to a great extent by the natural errors that the convolutional neural networks (CNN) may cause, these improvements are shown and supported by the implementation in real time through video links provided in this work.
AB - Drowsiness and driver distraction are considered the main causes of traffic accidents in the world. Considering this situation, this paper proposes two important modifications to our previously proposed driver drowsiness and distraction detector for real-time implementation on handheld mobile devices, such as smartphones. The first modification is due to a large variation in the capacity of mobile devices. To adapt the proposed system to a wide range of mobile devices, we present two automatic threshold calculations, which are used to differentiate driver drowsiness from normal blinking and dangerous driver distraction from normal short-term distraction. The second modification is related to the alarm during a continuous dangerous situation of the driver. We introduce a new algorithm to ensure the continuous activation of the alarm while the dangerous situation continues. These improvements perform as the general algorithm, since when it was implemented in mobile devices with low computational power, as well as in devices that do not have these limitations, the alarm activation times were not affected; On the other hand, it was possible to increase the accuracy originally given by the first system with respect to Ground Truth by almost 25% on average, resulting in alarm activations not being affected to a great extent by the natural errors that the convolutional neural networks (CNN) may cause, these improvements are shown and supported by the implementation in real time through video links provided in this work.
KW - Convolutional Neural Networks (CNN)
KW - Driver distraction detection
KW - Driver drowsiness detection
KW - Mobile devices
KW - Real-time
KW - portable implementation
UR - http://www.scopus.com/inward/record.url?scp=85139759413&partnerID=8YFLogxK
U2 - 10.3233/FAIA220258
DO - 10.3233/FAIA220258
M3 - Contribución a la conferencia
AN - SCOPUS:85139759413
T3 - Frontiers in Artificial Intelligence and Applications
SP - 272
EP - 285
BT - New Trends in Intelligent Software Methodologies, Tools and Techniques - Proceedings of the 21st International Conference on New Trends in Intelligent Software Methodologies, Tools and Techniques, SoMeT 2022
A2 - Fujita, Hamido
A2 - Watanobe, Yutaka
A2 - Azumi, Takuya
PB - IOS Press BV
T2 - 21st International Conference on New Trends in Intelligent Software Methodologies, Tools and Techniques, SoMeT 2022
Y2 - 20 September 2022 through 22 September 2022
ER -