The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Kami membangunkan rangkaian saraf dalam (DNN) untuk mengesan rasa mengantuk pemandu dalam video. Model DNN yang dicadangkan yang menerima muka pemandu yang diekstrak daripada bingkai video sebagai input terdiri daripada tiga komponen - rangkaian saraf konvolusi (CNN), rangkaian saraf berulang berasaskan gerbang kawalan konvolusi (ConvCGRNN) dan lapisan undian. CNN adalah untuk mempelajari representasi muka daripada muka global yang kemudiannya disalurkan kepada ConvCGRNN untuk mempelajari kebergantungan temporal mereka. Lapisan pengundian berfungsi seperti himpunan banyak pengelas kecil untuk meramalkan keadaan mengantuk. Keputusan percubaan pada set data NTHU-DDD menunjukkan bahawa model kami bukan sahaja mencapai ketepatan kompetitif sebanyak 84.81% tanpa sebarang pasca pemprosesan tetapi ia boleh berfungsi dalam masa nyata dengan kelajuan tinggi kira-kira 100 fps.
Toan H. VU
National Central University
An DANG
National Central University
Jia-Ching WANG
National Central University,Pervasive Artificial Intelligence Research (PAIR) Labs
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Salinan
Toan H. VU, An DANG, Jia-Ching WANG, "A Deep Neural Network for Real-Time Driver Drowsiness Detection" in IEICE TRANSACTIONS on Information,
vol. E102-D, no. 12, pp. 2637-2641, December 2019, doi: 10.1587/transinf.2019EDL8079.
Abstract: We develop a deep neural network (DNN) for detecting driver drowsiness in videos. The proposed DNN model that receives driver's faces extracted from video frames as inputs consists of three components - a convolutional neural network (CNN), a convolutional control gate-based recurrent neural network (ConvCGRNN), and a voting layer. The CNN is to learn facial representations from global faces which are then fed to the ConvCGRNN to learn their temporal dependencies. The voting layer works like an ensemble of many sub-classifiers to predict drowsiness state. Experimental results on the NTHU-DDD dataset show that our model not only achieve a competitive accuracy of 84.81% without any post-processing but it can work in real-time with a high speed of about 100 fps.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2019EDL8079/_p
Salinan
@ARTICLE{e102-d_12_2637,
author={Toan H. VU, An DANG, Jia-Ching WANG, },
journal={IEICE TRANSACTIONS on Information},
title={A Deep Neural Network for Real-Time Driver Drowsiness Detection},
year={2019},
volume={E102-D},
number={12},
pages={2637-2641},
abstract={We develop a deep neural network (DNN) for detecting driver drowsiness in videos. The proposed DNN model that receives driver's faces extracted from video frames as inputs consists of three components - a convolutional neural network (CNN), a convolutional control gate-based recurrent neural network (ConvCGRNN), and a voting layer. The CNN is to learn facial representations from global faces which are then fed to the ConvCGRNN to learn their temporal dependencies. The voting layer works like an ensemble of many sub-classifiers to predict drowsiness state. Experimental results on the NTHU-DDD dataset show that our model not only achieve a competitive accuracy of 84.81% without any post-processing but it can work in real-time with a high speed of about 100 fps.},
keywords={},
doi={10.1587/transinf.2019EDL8079},
ISSN={1745-1361},
month={December},}
Salinan
TY - JOUR
TI - A Deep Neural Network for Real-Time Driver Drowsiness Detection
T2 - IEICE TRANSACTIONS on Information
SP - 2637
EP - 2641
AU - Toan H. VU
AU - An DANG
AU - Jia-Ching WANG
PY - 2019
DO - 10.1587/transinf.2019EDL8079
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E102-D
IS - 12
JA - IEICE TRANSACTIONS on Information
Y1 - December 2019
AB - We develop a deep neural network (DNN) for detecting driver drowsiness in videos. The proposed DNN model that receives driver's faces extracted from video frames as inputs consists of three components - a convolutional neural network (CNN), a convolutional control gate-based recurrent neural network (ConvCGRNN), and a voting layer. The CNN is to learn facial representations from global faces which are then fed to the ConvCGRNN to learn their temporal dependencies. The voting layer works like an ensemble of many sub-classifiers to predict drowsiness state. Experimental results on the NTHU-DDD dataset show that our model not only achieve a competitive accuracy of 84.81% without any post-processing but it can work in real-time with a high speed of about 100 fps.
ER -