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Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.
HybridFatigue: A Real-time Driver Drowsiness Detection using Hybrid Features and Transfer Learning
Digital Object Identifier (DOI) : 10.14569/IJACSA.2020.0110173
Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 11 Issue 1, 2020.
Abstract: Road accidents mainly caused by the state of driver drowsiness. Detection of driver drowsiness (DDD) or fatigue is an important and challenging task to save road-side accidents. To help reduce the mortality rate, the “HybridFatigue” DDD system was proposed. This HybridFatigue system is based on integrating visual features through PERCLOS measure and non-visual features by heart-beat (ECG) sensors. A hybrid system was implemented to combine both visual and non-visual features. Those hybrid features have been extracted and classified as driver fatigue by advanced deep-learning-based architectures in real-time. A multi-layer based transfer learning approach by using a convolutional neural network (CNN) and deep-belief network (DBN) was used to detect driver fatigue from hybrid features. To solve night-time driving and to get accurate results, the ECG sensors were utilized on steering by analyzing heartbeat signals in case if the camera is not enough to get facial features. Also to solve the accurate detection of center head-position of drivers, two-cameras were mounted instead of a single camera. As a result, a new HybridFatigue system was proposed to get high accuracy of driver's fatigue. To train and test this HybridFatigue system, three online datasets were used. Compare to state-of-the-art DDD system, the HybridFatigue system is outperformed. On average, the HybridFatigue system achieved 94.5% detection accuracy on 4250 images when tested on different subjects in the variable environment. The experimental results indicate that the HybridFatigue system can be utilized to decrease accidents.
Keywords: Driver fatigue; image processing; deep learning; transfer learning; convolutional neural network; deep belief network
Qaisar Abbas, “HybridFatigue: A Real-time Driver Drowsiness Detection using Hybrid Features and Transfer Learning” International Journal of Advanced Computer Science and Applications(IJACSA), 11(1), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0110173
@article{Abbas2020,
title = {HybridFatigue: A Real-time Driver Drowsiness Detection using Hybrid Features and Transfer Learning},
journal = {International Journal of Advanced Computer Science and Applications}
doi = {10.14569/IJACSA.2020.0110173},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0110173},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {1},
author = {Qaisar Abbas},
}
