Open AccessFatigue driving recognition network: fatigue driving recognition via convolutional neural network and long short‐term memory units
Author(s) -
Xiao Zhitao,
Hu Zhiqiang,
Geng Lei,
Zhang Fang,
Wu Jun,
Li Yuelong
Publication year - 2019
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/iet-its.2018.5392
Subject(s) - convolutional neural network , computer science , artificial intelligence , feature (linguistics) , deep learning , pattern recognition (psychology) , long short term memory , task (project management) , term (time) , feature extraction , sequence (biology) , state (computer science) , recurrent neural network , computer vision , artificial neural network , engineering , physics , quantum mechanics , philosophy , linguistics , systems engineering , algorithm , biology , genetics
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.