Driver Fatigue Detection Method Based on Eye Multi-Feature Fusion
Qinghua Liu, Haixiao Zhong, Xuehan Zhao, Lu Sun
Available Online March 2018.
- https://doi.org/10.2991/iceea-18.2018.53How to use a DOI?
- face detection; random forest regression model; PERCLOS; CART; fatigue detection
- In order to improve the accuracy of driver fatigue detection, this paper proposes a new fatigue detection method based on random forest regression model and integrate eye fatigue surveillance method for multiple characteristic parameters. First, we use the features of simple class Haar to cascade algorithm Adaboost, aiming at carrying out quick face location detection. To solve the problem of low accuracy and high misclassification rate when uses the conventional method PERCLOS (percentage of eyelid closure over the pupil over time) via single parameter judgment, the paper increases four new parameters while extracting the eye fatigue characteristics and uses the classification and regression tree(CART) to judge whether the driver is awake or tired. The experimental results show that the accuracy rate of the driver's fatigue test is 96.7%, the error rate of the driver is 1.7% when the driver is in the fatigue state, the processing speed of our driver fatigue detection system is about 30 frames per second, it can make a judgment about the driver’s fatigue state in about 10 seconds, which owns higher accuracy and real-time.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Qinghua Liu AU - Haixiao Zhong AU - Xuehan Zhao AU - Lu Sun PY - 2018/03 DA - 2018/03 TI - Driver Fatigue Detection Method Based on Eye Multi-Feature Fusion BT - 2018 2nd International Conference on Electrical Engineering and Automation (ICEEA 2018) PB - Atlantis Press SP - 241 EP - 244 SN - 2352-5401 UR - https://doi.org/10.2991/iceea-18.2018.53 DO - https://doi.org/10.2991/iceea-18.2018.53 ID - Liu2018/03 ER -