计算机工程 ›› 2012, Vol. 38 ›› Issue (9): 189-192.doi: 10.3969/j.issn.1000-3428.2012.09.057

• 人工智能及识别技术 • 上一篇    下一篇

基于贝叶斯网络的疲劳度及注意力检测

张建明a,魏林峰a,刘志强b,汪 澎b   

  1. (江苏大学 a. 计算机科学与通信工程学院;b. 汽车与交通工程学院,江苏 镇江 212013)
  • 收稿日期:2011-06-28 出版日期:2012-05-05 发布日期:2012-05-05
  • 作者简介:张建明(1964-),男,教授、博士,主研方向:虚拟现实,图像处理;魏林峰,硕士研究生;刘志强,教授、博士导师;汪 澎,博士研究生
  • 基金项目:
    江苏省自然科学基金资助项目(BK2009199)

Fatigue and Attention Detection Based on Bayesian Network

ZHANG Jian-ming a, WEI Lin-feng a, LIU Zhi-qiang b, WANG Peng b   

  1. (a. School of Computer Science and Telecommunication Engineering; b. School of Automobile and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China)
  • Received:2011-06-28 Online:2012-05-05 Published:2012-05-05

摘要: 为提高基于单一特征检测算法的准确率和可靠性,提出基于贝叶斯网络融合多个特征参数的检测算法。定位眼睛和嘴巴,利用两眼和嘴巴组成的三角形建立头部旋转模型,提取各特征的参数并用贝叶斯网络进行融合,用来判断驾驶员的驾驶状态,当出现非正常驾驶状态时给以警告。实验结果表明,该算法对于检测出驾驶员的疲劳度和注意力分散状态有较高的准确性。

关键词: 驾驶疲劳度, 眼睛检测, 嘴巴检测, Otsu算法, 贝叶斯网络, 数据融合

Abstract: In order to improve accuracy and reliability of the detection based on a single feature, a detection algorithm based on Bayesian network fusing multiple features parameters is proposed. The algorithm locates driver’s eyes and mouth, creates head revolution pattern, which is based on the triangle composing of two eyes and mouth, then inputs all the extracted character parameters to Bayesian network and makes a logic judgment, a timely warning is given when detecting drivers in an abnormal driving status. Experimental results show that the algorithm for detecting drowsy or losing attention has high accuracy.

Key words: drive fatigue, eye detection, mouth detection, Otsu algorithm, Bayesian network, data fusion

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