EEG-Based Driver Drowsiness Detection Using the Dynamic Time Dependency Method

2019 ◽  
pp. 39-47
Author(s):  
Haolan Zhang ◽  
Qixin Zhao ◽  
Sanghyuk Lee ◽  
Margaret G. Dowens
Keyword(s):  
Time Dependency ◽  
Drowsiness Detection ◽  
Driver Drowsiness ◽  
Dynamic Time

scholarly journals Driver Drowsiness Estimation Based on Factorized Bilinear Feature Fusion and a Long-Short-Term Recurrent Convolutional Network

Information ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 3
Author(s):  
Shuang Chen ◽  
Zengcai Wang ◽  
Wenxin Chen
Keyword(s):  
Short Term Memory ◽  
Feature Fusion ◽  
Detection Methods ◽  
Video Frame ◽  
Estimation Model ◽  
Short Term ◽  
Convolutional Network ◽  
Drowsiness Detection ◽  
Driver Drowsiness ◽  
Time Information

The effective detection of driver drowsiness is an important measure to prevent traffic accidents. Most existing drowsiness detection methods only use a single facial feature to identify fatigue status, ignoring the complex correlation between fatigue features and the time information of fatigue features, and this reduces the recognition accuracy. To solve these problems, we propose a driver sleepiness estimation model based on factorized bilinear feature fusion and a long- short-term recurrent convolutional network to detect driver drowsiness efficiently and accurately. The proposed framework includes three models: fatigue feature extraction, fatigue feature fusion, and driver drowsiness detection. First, we used a convolutional neural network (CNN) to effectively extract the deep representation of eye and mouth-related fatigue features from the face area detected in each video frame. Then, based on the factorized bilinear feature fusion model, we performed a nonlinear fusion of the deep feature representations of the eyes and mouth. Finally, we input a series of fused frame-level features into a long-short-term memory (LSTM) unit to obtain the time information of the features and used the softmax classifier to detect sleepiness. The proposed framework was evaluated with the National Tsing Hua University drowsy driver detection (NTHU-DDD) video dataset. The experimental results showed that this method had better stability and robustness compared with other methods.

Driver drowsiness detection using facial dynamic fusion information and a DBN

2018 ◽  
Vol 12 (2) ◽  
pp. 127-133 ◽  
Author(s):  
Lei Zhao ◽  
Zengcai Wang ◽  
Xiaojin Wang ◽  
Qing Liu
Keyword(s):  
Drowsiness Detection ◽  
Driver Drowsiness

An Integrated Framework for Driver Drowsiness Detection and Alcohol Intoxication using Machine Learning

2021 ◽  
Author(s):  
Renju Rachel Varghese ◽  
Pramod Mathew Jacob ◽  
Joanna Jacob ◽  
Merlin Nissi Babu ◽  
Rupali Ravikanth ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alcohol Intoxication ◽  
Integrated Framework ◽  
Drowsiness Detection ◽  
Driver Drowsiness

Two-Stream Spatial-Temporal Graph Convolutional Networks for Driver Drowsiness Detection

2021 ◽  
pp. 1-13
Author(s):  
Jing Bai ◽  
Wentao Yu ◽  
Zhu Xiao ◽  
Vincent Havyarimana ◽  
Amelia C. Regan ◽  
...  
Keyword(s):  
Drowsiness Detection ◽  
Convolutional Networks ◽  
Driver Drowsiness ◽  
Temporal Graph
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