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多特征融合的驾驶员疲劳检测研究

黄占鳌 史晋芳

黄占鳌, 史晋芳. 多特征融合的驾驶员疲劳检测研究[J]. 机械科学与技术, 2018, 37(11): 1750-1754. doi: 10.13433/j.cnki.1003-8728.20180131
引用本文: 黄占鳌, 史晋芳. 多特征融合的驾驶员疲劳检测研究[J]. 机械科学与技术, 2018, 37(11): 1750-1754. doi: 10.13433/j.cnki.1003-8728.20180131
Huang Zhan'ao, Shi Jinfang. Research on Driver's Fatigue Detection by Multi-feature Fusion[J]. Mechanical Science and Technology for Aerospace Engineering, 2018, 37(11): 1750-1754. doi: 10.13433/j.cnki.1003-8728.20180131
Citation: Huang Zhan'ao, Shi Jinfang. Research on Driver's Fatigue Detection by Multi-feature Fusion[J]. Mechanical Science and Technology for Aerospace Engineering, 2018, 37(11): 1750-1754. doi: 10.13433/j.cnki.1003-8728.20180131

多特征融合的驾驶员疲劳检测研究

doi: 10.13433/j.cnki.1003-8728.20180131
基金项目: 

西南科技大学博士基金项目(16ZX7119)资助

详细信息
    作者简介:

    黄占鳌(1993-),硕士研究生,研究方向为图像处理、机器学习,huangza123@qq.com

    通讯作者:

    史晋芳,副教授,硕士生导师,博士研究生,jennifer.shi@qq.com

Research on Driver's Fatigue Detection by Multi-feature Fusion

  • 摘要: 针对驾驶疲劳检测中面部特征定位及驾驶员疲劳状态判别方法判断存在的不足,提出了利用监督下降算法同时定位驾驶员的多个面部特征。在眨眼、哈欠及点头判断的基础上,提取驾驶员眨眼频率、哈欠频率及点头频率多个特征值建立疲劳检测样本数据库,并构建朴素贝叶斯分类器进行疲劳判断。当驾驶员出现疲劳驾驶时及时给以警告信息,以预防交通事故发生。在实际的驾驶环境视频测试结果中,驾驶员疲劳状态的判别平均准确率达到了94.87%,具有较好的性能。
  • [1] 肖赛,雷叶维.驾驶疲劳致因及监测研究进展[J].交通科技与经济,2017,19(4):14-19,63 Xiao S, Lei Y W. Research on the causes for driver fatigue and the monitoring technology progress[J]. Technology & Economy in Areas of Communications, 2017,19(4):14-19,63(in Chinese)
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出版历程
  • 收稿日期:  2018-01-18
  • 刊出日期:  2018-11-05

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