TY - GEN
T1 - Recognizing driver inattention by convolutional neural networks
AU - Yan, Chao
AU - Jiang, Huiying
AU - Zhang, Bailing
AU - Coenen, Frans
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2016/2/16
Y1 - 2016/2/16
N2 - Driver inattention has long been recognized as the main contributing factors in traffic accidents. Development of intelligent driver assistance systems with embedded functionality of driver vigilance monitoring is therefore an urgent and challenging task. This paper presents a novel system which applies convolutional neural network to automatically learn and predict the state of driver's eye, mouth and ear. The initial inspiration is to predict driver fatigue and distraction by analysing these states. In our works, a CNN model was trained with six classes of labeled data. The Approach was verified using self-specified Driving Dataset, which comprised of four activities, including normal driving, responding to a cell phone call, eating and falling asleep. Experiment results demonstrate that our design achieves a promising performance with a overall accuracy of 95.56% in classifying six states of the driver's eye, mouth and ear.
AB - Driver inattention has long been recognized as the main contributing factors in traffic accidents. Development of intelligent driver assistance systems with embedded functionality of driver vigilance monitoring is therefore an urgent and challenging task. This paper presents a novel system which applies convolutional neural network to automatically learn and predict the state of driver's eye, mouth and ear. The initial inspiration is to predict driver fatigue and distraction by analysing these states. In our works, a CNN model was trained with six classes of labeled data. The Approach was verified using self-specified Driving Dataset, which comprised of four activities, including normal driving, responding to a cell phone call, eating and falling asleep. Experiment results demonstrate that our design achieves a promising performance with a overall accuracy of 95.56% in classifying six states of the driver's eye, mouth and ear.
KW - Driving assistance system
KW - Driving inattention recognition
UR - http://www.scopus.com/inward/record.url?scp=84966546468&partnerID=8YFLogxK
U2 - 10.1109/CISP.2015.7407964
DO - 10.1109/CISP.2015.7407964
M3 - Conference Proceeding
AN - SCOPUS:84966546468
T3 - Proceedings - 2015 8th International Congress on Image and Signal Processing, CISP 2015
SP - 680
EP - 685
BT - Proceedings - 2015 8th International Congress on Image and Signal Processing, CISP 2015
A2 - Wang, Lipo
A2 - Lin, Sen
A2 - Tao, Zhiyong
A2 - Zeng, Bing
A2 - Hui, Xiaowei
A2 - Shao, Liangshan
A2 - Liang, Jie
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th International Congress on Image and Signal Processing, CISP 2015
Y2 - 14 October 2015 through 16 October 2015
ER -
