Abstract
Drivers’ fatigue is considered one of the main causes of fatal accidents. Its detection can be a challenging task especially in complex environment. In this work, we suggest a real-time and low-cost framework, its main goal is to augment the drivers ‘safety by detecting driver ‘fatigue using an embedded camera and deep learning techniques. The proposed approach starts with face and landmark localization using a multi-tasking convolutional neural network (MTCNN). Next, the eye region extraction, the eye status recognition using an optimized convolutional neural network and finally a fatigue judgment model is developed that lies on eye blinking counter technic. Our model is trained and tested using public face datasets, which made it possible to reach a higher accuracy of 94,84% compared to other existing works in the litterature. The final output of the proposed framework are notifications and alerts sent to the drivers in case of drowsiness situation.
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Boucetta, Z., El Fazziki, A., El Adnani, M. (2021). Deep Learning Based Driver’s Fatigue Detection Framework. In: Ben Ahmed, M., Rakıp Karaș, İ., Santos, D., Sergeyeva, O., Boudhir, A.A. (eds) Innovations in Smart Cities Applications Volume 4. SCA 2020. Lecture Notes in Networks and Systems, vol 183. Springer, Cham. https://doi.org/10.1007/978-3-030-66840-2_28
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DOI: https://doi.org/10.1007/978-3-030-66840-2_28
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