基于高斯眼白模型的疲劳驾驶检测

目的 为解决疲劳驾驶检测中人眼状态识别的难点，提出一种基于眼白分割的疲劳检测方法。方法 首先对获取图像进行人脸检测，利用眼白在Cb-Cr上良好的聚类性，基于YCbCr颜色空间建立高斯眼白分割模型；然后在人脸区域图像内做眼白分割，计算眼白面积；最后将眼白面积作为人眼开度指标，结合PERCLOS（percentage of eyelid closure over the pupil over time）判定人的疲劳状态。结果 选取10个短视频进行采帧分析，实验结果表明，高斯眼白分割模型能有效分离眼白，并识别人眼开合状态，准确率可达96.77%。结论 在良好光线条件下，本文方法能取得不错的分割效果；本文所提出的以眼白面积作为判定人眼开度的指标，能准确地判定人的疲劳状态。实验结果证明了该方法的有效性，值得今后做更深入的研究。

Fatigue driving detection based on sclera Gaussian model

Objective Considering that achieving non-contact and high precision in fatigue driving detection is difficult, the method based on computer vision arises as a possible solution. The key point to achieve fatigue driving detection is to recognize the open and closed state of driver''s eyes and estimate if the driver is fatigued and sleepy. This paper proposes a new fatigue driving detection method based on sclera segmentation to solve the problem of recognizing the open and closed state of driver''s eyes in fatigue driving. Method The face is tested from the acquired picture using a face detection method based on Adaboost raised by Viola and Jones. Then, a Gauss sclera model based on YCbCr color space is built for the next step because of the good clustering performance of sclera in Cb-Cr color space. The sclera is detected in the face region using this model and the area of the sclera is calculated. The sclera area is considered the eye opening index and the fatigue state is determined along with the PERCLOS criterion. Result Experimental results for 10 short testing videos show that the proposed algorithm can select the sclera part and recognize the open and closed state of human eyes effectively. The accuracy rate can reach up to 96.77%. Conclusion The proposed method can achieve satisfactory segmentation effect under good lighting condition. Using the sclera area as the eye opening index, the proposed method can also judge the state of driver''s fatigue accurately. The new approach is effective and must be further studied.