A Fatigue Driving Detection Algorithm Based on Facial Motion Information Entropy

Research studies on machine vision-based driver fatigue detection algorithm have improved traffic safety significantly. Generally, many algorithms asses the driving state according to limited video frames, thus resulting in some inaccuracy. We propose a real-time detection algorithm involved in info...

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Bibliographic Details
Published in:Journal of advanced transportation 2020, Vol.2020 (2020), p.1-17
Main Authors: Liang, Jianzhong, Tu, Haiqing, Gong, Yunbo, You, Feng, Wang, Haiwei
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Analysis
Artificial neural networks
Cable television broadcasting industry
Centroids
Computer simulation
Datasets
Driver fatigue
Driving ability
Driving conditions
Entropy
Entropy (Information theory)
Fatigue
Feature extraction
Frames (data processing)
Machine vision
Methods
Neural networks
Physiology
Real time
Toolkits
Traffic
Traffic accidents & safety
Traffic safety
Transportation
Triangles
Vision systems
Wavelet transforms
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Summary:Research studies on machine vision-based driver fatigue detection algorithm have improved traffic safety significantly. Generally, many algorithms asses the driving state according to limited video frames, thus resulting in some inaccuracy. We propose a real-time detection algorithm involved in information entropy. Particularly, this algorithm relies on the analysis of sufficient consecutive video frames. First, we introduce an improved YOLOv3-tiny convolutional neural network to capture the facial regions under complex driving conditions, eliminating the inaccuracy and affections caused by artificial feature extraction. Second, we construct a geometric area called Face Feature Triangle (FFT) based on the application of the Dlib toolkit as well as the landmarks and the coordinates of the facial regions; then we create a Face Feature Vector (FFV), which contains all the information of the area and centroid of each FFT. We use FFV as an indicator to determine whether the driver is in fatigue state. Finally, we design a sliding window to get the facial information entropy. Comparative experiments show that our algorithm performs better than the current ones on both accuracy and real-time performance. In simulated driving applications, the proposed algorithm detects the fatigue state at a speed of over 20 fps with an accuracy of 94.32%.
ISSN:0197-6729
2042-3195
DOI:10.1155/2020/8851485