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A Real-time Driving Drowsiness Detection Algorithm With Individual Differences Consideration
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DOI：10.1109/ACCESS.2019.2958667.png)
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摘要
En 中文
The research work about driving drowsiness detection algorithm has great significance to improve traffic safety. Presently, there are many fruits and literature about driving drowsiness detection method. However, most of them are devoted to find a universal drowsiness detection method, while ignore the individual driver differences. This paper proposes a real-time driving drowsiness detection algorithm that considers the individual differences of driver. A deep cascaded convolutional neural network was constructed to detect the face region, which avoids the problem of poor accuracy caused by artificial feature extraction. Based on the Dlib toolkit, the landmarks of frontal driver facial in a frame are found. According to the eyes landmarks, a new parameter, called Eyes Aspect Ratio, is introduced to evaluate the drowsiness of driver in the current frame. Taking into account differences in size of driver's eyes, the proposed algorithm consists of two modules: offline training and online monitoring. In the first module, a unique fatigue state classifier, based on Support Vector Machines, was trained which taking the Eyes Aspect Ratio as input. Then, in the second module, the trained classifier is application to monitor the state of driver online. Because the fatigue driving state is gradually produced, a variable which calculated by number of drowsy frames per unit time is introduced to assess the drowsiness of driver. Through comparative experiments, we demonstrate this algorithm outperforms current driving drowsiness detection approaches in both accuracy and speed. In simulated driving applications, the proposed algorithm detects the drowsy state of driver quickly from 640*480 resolution images at over 20fps and 94.80% accuracy. The research result can serve intelligent transportation system, ensure driver safety and reduce the losses caused by drowsy driving.
Keyword:
Traffic safety
driving drowsiness detection
CNN
individual differences
SVM
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期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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