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

Content Provider	IET Digital Library
Author	Xiao, Zhitao Hu, Zhiqiang Geng, Lei Zhang, Fang Wu, Jun Li, Yuelong
Abstract	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.
Starting Page	1410
Ending Page	1416
Page Count	7
ISSN	1751956X
Volume Number	13
e-ISSN	17519578
Issue Number	Issue 9, Sep (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-its/13/9
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-its.2018.5392
Journal	IET Intelligent Transport Systems
Publisher Date	2019-05-15
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Authors Computer Vision And Image Processing Technique Deep Cascaded Multitask Framework Deep Convolutional Layer Driver Effective Method End-to-end Trainable Convolutional Neural Network Eye Region Fatigue Fatigue Detection Fatigue Driving Recognition Network Fatigue State Feature Extraction Filtering Method in Signal Processing Filtering Theory Image Recognition Image-based Sequence Recognition Knowledge Engineering Technique Learning in AI LSTM Units Neural Computing Technique Neural Nets Optical, Image And Video Signal Processing Sequence-level Prediction Short-term Memory Units Spatial Feature Spatial–temporal Feature State-of-the-art Technique Statistics Traffic Accidents
Content Type	Text
Resource Type	Article
Subject	Law Transportation Environmental Science Mechanical Engineering

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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