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.
Fatigue driving recognition network: fatigue driving recognition via convolutional neural network and long short-term memory units
IET Intelligent Transport Systems ; 13 , 9 ; 1410-1416
2019-06-06
7 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
sequence-level prediction , deep convolutional layers , fatigue driving recognition network , filtering theory , traffic accidents , spatial–temporal feature , learning (artificial intelligence) , authors , fatigue detection , effective method , LSTM units , end-to-end trainable convolutional neural network , neural nets , driver , fatigue state , deep cascaded multitask framework , eye region , image-based sequence recognition , fatigue , feature extraction , short-term memory units , state-of-the-art techniques , spatial features
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