Abstract
The research on driver fatigue detection is of great significance to improve driving safety. This paper proposes a real-time comprehensive driver fatigue detection algorithm based on facial landmarks to improve the detection accuracy, which detects the driver’s fatigue status by using facial video sequences without equipping their bodies with other intelligent devices. A tasks-constrained deep convolutional network is constructed to detect the face region based on 68 key points, which can solve the optimization problem caused by the different convergence speeds of each task. According to the real-time facial video images, the eye feature of the eye aspect ratio (EAR), mouth aspect ratio (MAR) and percentage of eye closure time (PERCLOS) are calculated based on facial landmarks. A comprehensive driver fatigue assessment model is established to assess the fatigue status of drivers through eye/mouth feature selection. After a series of comparative experiments, the results show that this proposed algorithm achieves good performance in both accuracy and speed for driver fatigue detection.
Highlights
Driver fatigue, or drowsiness, contributes to many thousands of deaths and injuries on the roads every year
We first evaluate the effectiveness of the proposed tasks-constrained deep convolutional network (TCDCN) in the face detection dataset and benchmark (FDDB) [36], and discuss the correlation between
Research on fatigue driving detection technology is the top priority for research in reducing traffic accidents caused by fatigue, which is of great significance to traffic safety
Summary
Drowsiness, contributes to many thousands of deaths and injuries on the roads every year. Recent research suggests that driver fatigue detection methodologies can be used to prevent such accidents [5]. The second is the abnormal fatigue detection in which a driver is deemed to be asleep and the system can actively control the vehicle to avoid accidents [8,9,10]. Researchers have developed methodologies to detect or indicate the driver fatigue state prior to a collision [11,12,13]. Observation of the driver behavior method may be determined by continuous recording through a camera installed in the vehicle to monitor eye-closure time, eye blinking frequency, movement and pose of the head, and yawing, etc. We propose a real-time observation of the driver behavior method to detect driver fatigue.
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