Fatigue driving recognition network: fatigue driving recognition via convolutional neural network and long short-term memory units

Fatigue driving has become one of the major causes of traffic accidents. The authors propose an effective method capable of detecting fatigue state via the spatial-temporal feature of driver's eyes. In this work, the authors consider fatigue detection as image-based sequence recognition and an end-to-end trainable convolutional neural network with long short-term memory (LSTM) units is designed. First, the authors apply a deep cascaded multi-task framework to extract eye region from infrared videos. Then the spatial features are learned by deep convolutional layers and the relationships between adjacent frames are analysed via LSTM units. Finally, through authors' model, a sequence-level prediction for driving state is produced. The proposed method achieves superior accuracy over the state-of-the-art techniques on authors' own dataset. Experimental results demonstrate the feasibility of authors' method.

• Record URL:
  • https://doi.org/10.1049/iet-its.2018.5392
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/1751956X
• Supplemental Notes:
  • Abstract reprinted with permission of the Institution of Engineering and Technology.
• Authors:
  • Xiao, Zhitao
  • Hu, Zhiqiang
  • Geng, Lei
  • Zhang, Fang
  • Wu, Jun
  • Li, Yuelong
• Publication Date: 2019-9

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 1410-1416
• Serial:
  • IET Intelligent Transport Systems
  • Volume: 13
  • Issue Number: 9
  • Publisher: Institution of Engineering and Technology (IET)
  • ISSN: 1751-956X
  • EISSN: 1751-9578
  • Serial URL: https://ietresearch.onlinelibrary.wiley.com/journal/17519578
• Publication flags:

  Open Access (libre)

Subject/Index Terms

• TRT Terms: Artificial intelligence; Driving; Highway safety; Memory; Neural networks; Physiological fatigue; Traffic crashes
• Subject Areas: Highways; Safety and Human Factors; Vehicles and Equipment;

Filing Info

• Accession Number: 01727081
• Record Type: Publication
• Files: TRIS
• Created Date: Dec 31 2019 4:30PM