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Driver Fatigue Detection Based On Facial Feature Analysis
International Journal of Pattern Recognition and Artificial Intelligence ( IF 1.5 ) Pub Date : 2021-09-08 , DOI: 10.1142/s0218001421500348 Yimin Zhang 1 , Xianwei Han 1 , Wei Gao 1 , Yunliang Hu 2
International Journal of Pattern Recognition and Artificial Intelligence ( IF 1.5 ) Pub Date : 2021-09-08 , DOI: 10.1142/s0218001421500348 Yimin Zhang 1 , Xianwei Han 1 , Wei Gao 1 , Yunliang Hu 2
Affiliation
Fatigue driving is one of the main causes of traffic accidents. In recent years, considerable attention has been paid to fatigue detection systems, which is an important solution for preventing fatigue driving. In order to prevent and reduce fatigue driving, a driver fatigue detection system based on computer vision is proposed. In this system, an improved face detection method is used to detect the driver’s face from the image obtained by a charge coupled device (CCD) camera. Then, the feature points of the eyes and mouth are located by an ensemble of regression trees. Next, fatigue characteristic parameters are calculated by the improved percentage of eyelid closure over the pupil over time algorithm. Finally, the state of drivers is evaluated by using a fuzzy neural network. The system can effectively monitor and remind the state of drivers so as to significantly avoid or decrease the occurrence of traffic accidents. The experimental results show that the system is of wonderful real-time performance and accurate recognition rate, so it meets the requirements of practicality in driver fatigue detection greatly.
中文翻译:
基于面部特征分析的驾驶员疲劳检测
疲劳驾驶是造成交通事故的主要原因之一。近年来,疲劳检测系统受到了广泛关注,这是防止疲劳驾驶的重要解决方案。为了预防和减少疲劳驾驶,提出了一种基于计算机视觉的驾驶员疲劳检测系统。在该系统中,改进的人脸检测方法用于从电荷耦合器件(CCD)相机获得的图像中检测驾驶员的面部。然后,眼睛和嘴巴的特征点由回归树的集合定位。接下来,疲劳特征参数通过瞳孔随时间变化的改进的眼睑闭合百分比算法来计算。最后,使用模糊神经网络评估驾驶员的状态。该系统可以有效地监控和提醒驾驶员的状态,从而显着避免或减少交通事故的发生。实验结果表明,该系统具有良好的实时性和准确的识别率,极大地满足了驾驶员疲劳检测的实用性要求。
更新日期:2021-09-08
中文翻译:
基于面部特征分析的驾驶员疲劳检测
疲劳驾驶是造成交通事故的主要原因之一。近年来,疲劳检测系统受到了广泛关注,这是防止疲劳驾驶的重要解决方案。为了预防和减少疲劳驾驶,提出了一种基于计算机视觉的驾驶员疲劳检测系统。在该系统中,改进的人脸检测方法用于从电荷耦合器件(CCD)相机获得的图像中检测驾驶员的面部。然后,眼睛和嘴巴的特征点由回归树的集合定位。接下来,疲劳特征参数通过瞳孔随时间变化的改进的眼睑闭合百分比算法来计算。最后,使用模糊神经网络评估驾驶员的状态。该系统可以有效地监控和提醒驾驶员的状态,从而显着避免或减少交通事故的发生。实验结果表明,该系统具有良好的实时性和准确的识别率,极大地满足了驾驶员疲劳检测的实用性要求。