Abstract
Abstract
Fatigue driving is one of the main causes of traffic accidents. This paper proposes a fatigue detection method based on computer vision. The first is the introduction of an optimized algorithm, based on AdaBoost, to detect the face area, and then the ERT algorithm is used to achieve precise localization of the facial landmarks. Finally, a variety of fatigue features of eyes and mouth state associated with driving fatigue are extracted, and after the fusion of all these features, the fatigue driving detection is performed. The experimental results show that multi-feature detection is more accurate than single feature detection.
Subject
General Physics and Astronomy
Reference14 articles.
1. Detection methods for a low-cost accelerometer-based approach for driver drowsiness detection;Lawoyin,2014
2. A Driver State Detection System— Combining a Capacitive Hand Detection Sensor With Physiological Sensors;Mühlbacher-Karrer;J. IEEE Transactions on Instrumentation and Measurement,2017
3. An EEG-based perceptual function integration network for application to drowsy driving;Chuang;J. Knowledge-Based Systems,2015
4. Vigilance Estimation Using a Wearable EOG Device in Real Driving Environment;Zheng;J. IEEE Transactions on Intelligent Transportation Systems,2020
5. Driving fatigue detection with fusion of EEG and forehead EOG;Huo,2016
Cited by
5 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献