Related papers: Drowsiness Detection Based On Driver Temporal Behavior Using a New Developed Dataset

Drowsiness Detection Based On Driver Temporal Behavior Using a New Developed Dataset

URL: http://arxiv.org/abs/2104.00125v1
Date: Wed, 31 Mar 2021 21:15:29 GMT
Title: Drowsiness Detection Based On Driver Temporal Behavior Using a New Developed Dataset
Authors: Farnoosh Faraji, Faraz Lotfi, Javad Khorramdel, Ali Najafi, Ali Ghaffari
Abstract summary: We apply YOLOv3 (You Look Only Once-version3) CNN for extracting facial features automatically. Then, LSTM neural network is employed to learn driver temporal behaviors including yawning and blinking time period. Results indicate the hybrid of CNN and LSTM ability in drowsiness detection and the effectiveness of the proposed method.
Score: 1.8811803364757564
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Driver drowsiness detection has been the subject of many researches in the past few decades and various methods have been developed to detect it. In this study, as an image-based approach with adequate accuracy, along with the expedite process, we applied YOLOv3 (You Look Only Once-version3) CNN (Convolutional Neural Network) for extracting facial features automatically. Then, LSTM (Long-Short Term Memory) neural network is employed to learn driver temporal behaviors including yawning and blinking time period as well as sequence classification. To train YOLOv3, we utilized our collected dataset alongside the transfer learning method. Moreover, the dataset for the LSTM training process is produced by the mentioned CNN and is formatted as a two-dimensional sequence comprised of eye blinking and yawning time durations. The developed dataset considers both disturbances such as illumination and drivers' head posture. To have real-time experiments a multi-thread framework is developed to run both CNN and LSTM in parallel. Finally, results indicate the hybrid of CNN and LSTM ability in drowsiness detection and the effectiveness of the proposed method.