| 基于视频图像和RBF神经网络的疲劳驾驶监测系统 |
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| 引用本文： | 王力,徐超,沈晓蓉,范耀祖.基于视频图像和RBF神经网络的疲劳驾驶监测系统[J].道路交通与安全,2009(4):30-33. |
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| 作者姓名： | 王力 徐超 沈晓蓉 范耀祖 |
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| 作者单位： | [1]北方工业大学机电工程学院自动化系,北京100144 [2]北京航空航天大学自动化学院控制系统与控制工程系,北京100083 |
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| 基金项目： | 北京市教委重点学科建设项目(BHBJZB-1-5); ;北京市科委“企业创新应用自主知识产权与技术标准试点”项目 |
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| 摘 要： | 采用视频采集方式和神经网络方法实现了驾驶员疲劳驾驶的非接触式监测。应用车头前端和车厢内部双路视频摄像头分别采集车辆相对于车道线的行驶轨迹和驾驶员的睁闭眼状态,应用Radon变换提取5 s内车头与车道线间的最大和最小偏离、相邻2帧间车头与车道线的最大角度变化量和平均角度差,应用AdaBoost算法提取驾驶员眼睛闭合帧数比例,并将上述各参数作为RBF神经网络的输入来实现驾驶员疲劳状态的动态监测,实验数据表明监测效果良好。
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| 关 键 词： | 疲劳驾驶监测 视频图像 径向基函数神经网络 Radon变换 AdaBoost算法 |
Driver Fatigue Monitoring System Based on RBF NN and Image Processing |
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| Wang Li,Xu Chao,Shen Xiaorong,Fan Yaozu.Driver Fatigue Monitoring System Based on RBF NN and Image Processing[J].Road Traffic & Safety,2009(4):30-33. |
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| Authors: | Wang Li Xu Chao Shen Xiaorong Fan Yaozu |
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| Institution: | Wang Li,Xu Chao,Shen Xiaorong,Fan Yaozu(School of Automation Science , Electrical Engineering,Beijing University of Aeronautics , Astronautics,Beijing 100083) |
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| Abstract: | As effective un-touched driver fatigue recognize tokens,driver's per eye close(PERCLOS)and vehicle's track has been used in this paper.Two cameras fixed at vehicle head and vehicle inside are used to capture the video stream of driver's face and vehicle track.Image processing technology is used to extract 5 parameters per 5 seconds,e.g.Radon transform applied to maximum and minimum distance from vehicle head to lane line and maximum and average angle difference from vehicle central line to lane line between... |
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| Keywords: | driver fatigue monitoring image processing RBF NN radon transform AdaBoost algorithm |
| 本文献已被 CNKI 维普 等数据库收录！ |
