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
2019-09-01
7 pages
Article (Journal)
Electronic Resource
English
fatigue , fatigue state , spatial features , LSTM units , image‐based sequence recognition , deep convolutional layers , driver , neural nets , fatigue driving recognition network , traffic accidents , eye region , end‐to‐end trainable convolutional neural network , effective method , spatial–temporal feature , deep cascaded multitask framework , feature extraction , state‐of‐the‐art techniques , learning (artificial intelligence) , fatigue detection , authors , short‐term memory units , filtering theory , sequence‐level prediction
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