Fatigue Driving Detection and Warning Based on Eye Features

  • Zhiwei ZhangEmail author
  • Ruijun Zhang
  • Jianguo Hao
  • Junsuo Qu
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 891)


For the aim of reducing the occurrence of traffic accidents caused by fatigue driving, it is of great significance to design a system based on eye features for fatigue driving detection and early warning. The system uses a camera to capture images, using an improved Haar feature cascade classification algorithm to detect the face area, and then uses a Ensemble of Regression Trees (ERT) cascade regression algorithm to detect human eyes and mark 12 points in the area. According to the Eye Aspect Ratio (EAR) algorithm and the blink frequency, the driver’s fatigue state can be determined and the alarm can be timely issued,and the image will be uploaded to the cloud platform of the Internet of things.


Face detection Feature extraction Fatigue driving Eye Aspect Ratio (EAR) 



This research was supported in part by grants from the International Cooperation and Exchange Program of Shaanxi Province (2018KW-026), Natural Science Foundation of Shaanxi Province (2018JM6120), Xi’an Science and Technology Plan Project (201805040YD18CG24(6)), Major Science and Technology Projects of XianYang City (2017k01-25-12), Graduate Innovation Fund of Xi’an University of Posts & Telecommunications (CXJJ2017012, CXJJ2017028, CXJJ2017056).


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Zhiwei Zhang
    • 1
    Email author
  • Ruijun Zhang
    • 1
  • Jianguo Hao
    • 1
  • Junsuo Qu
    • 2
  1. 1.School of Communication and EngineeringXi’an University of Post and TelecommunicationsXi’anChina
  2. 2.School of AutomationXi’an University of Posts and TelecommunicationsXi’anChina

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