Yang, J.H.,
Mao, Z.H.,
Tijerina, L.,
Pilutti, T.,
Coughlin, J.F.,
Feron, E.,
Detection of Driver Fatigue Caused by Sleep Deprivation,
SMC-A(39), No. 4, July 2009, pp. 694-705.
IEEE DOI
0906
BibRef
Fan, X.[Xiao],
Sun, Y.F.[Yan-Feng],
Yin, B.C.[Bao-Cai],
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Gabor-based dynamic representation for human fatigue monitoring in
facial image sequences,
PRL(31), No. 3, 1 February 2010, pp. 234-243.
Elsevier DOI
1001
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Earlier: A1, A2, A3, Only:
Multi-Scale Dynamic Human Fatigue Detection with Feature Level Fusion,
FG08(1-6).
IEEE DOI
0809
BibRef
And: A1, A3, A2, Only:
Dynamic Human Fatigue Detection Using Feature-Level Fusion,
ICISP08(94-102).
Springer DOI
0807
Human fatigue; Multi-scale; Dynamic feature; Feature fusion; AdaBoost algorithm
BibRef
Senaratne, R.[Rajinda],
Jap, B.[Budi],
Lal, S.[Sara],
Hsu, A.[Arthur],
Halgamuge, S.[Saman],
Fischer, P.[Peter],
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classification versus optical flow approach,
MVA(22), No. 4, July 2011, pp. 597-618.
WWW Link.
1107
BibRef
Zhao, C.,
Zhang, X.,
Zhang, B.,
Dang, Q.,
Lian, J.,
Driver's fatigue expressions recognition by combined features from
pyramid histogram of oriented gradient and contourlet transform
with random subspace ensembles,
IET-ITS(7), No. 1, 2013, pp. 36-45.
DOI Link
1307
BibRef
Hu, S.,
Zheng, G.,
Peters, B.,
Driver fatigue detection from electroencephalogram spectrum
after electrooculography artefact removal,
IET-ITS(7), No. 1, 2013, pp. 105-113.
DOI Link
1307
BibRef
Zhang, C.[Chi],
Wang, H.[Hong],
Fu, R.R.[Rong-Rong],
Automated Detection of Driver Fatigue Based on Entropy and Complexity
Measures,
ITS(15), No. 1, February 2014, pp. 168-177.
IEEE DOI
1403
accident prevention
BibRef
He, Q.C.[Qi-Chang],
Li, W.[Wei],
Fan, X.[Xiumin],
Fei, Z.M.[Zhi-Min],
Driver fatigue evaluation model with integration of multi-indicators
based on dynamic Bayesian network,
IET-ITS(9), No. 5, 2015, pp. 547-554.
DOI Link
1507
Bayes methods
BibRef
He, Q.C.[Qi-Chang],
Li, W.[Wei],
Fan, X.[Xiumin],
Fei, Z.M.[Zhi-Min],
Evaluation of driver fatigue with multi-indicators based on
artificial neural network,
IET-ITS(10), No. 8, 2016, pp. 555-561.
DOI Link
1610
driver information systems
BibRef
Alioua, N.[Nawal],
Amine, A.[Aouatif],
Rogozan, A.[Alexandrina],
Bensrhair, A.[Abdelaziz],
Rziza, M.[Mohammed],
Driver head pose estimation using efficient descriptor fusion,
JIVP(2016), No. 1, 2016, pp. 2.
DOI Link
1601
BibRef
Alioua, N.[Nawal],
Amine, A.[Aouatif],
Rziza, M.[Mohammed],
Aboutajdine, D.[Driss],
Driver's Fatigue and Drowsiness Detection to Reduce Traffic Accidents
on Road,
CAIP11(II: 397-404).
Springer DOI
1109
BibRef
Kaplan, S.,
Guvensan, M.A.,
Yavuz, A.G.,
Karalurt, Y.,
Driver Behavior Analysis for Safe Driving: A Survey,
ITS(16), No. 6, December 2015, pp. 3017-3032.
IEEE DOI
1512
Fatigue
BibRef
Li, Z.,
Sun, G.,
Zhang, F.,
Jia, L.,
Zheng, K.,
Zhao, D.,
Smartphone-based fatigue detection system using progressive locating
method,
IET-ITS(10), No. 3, 2016, pp. 148-156.
DOI Link
1604
driver information systems
BibRef
Mandal, B.,
Li, L.,
Wang, G.S.,
Lin, J.,
Towards Detection of Bus Driver Fatigue Based on Robust Visual
Analysis of Eye State,
ITS(18), No. 3, March 2017, pp. 545-557.
IEEE DOI
1703
Cameras
BibRef
Lee, B.G.,
Chong, T.W.,
Lee, B.L.,
Park, H.J.,
Kim, Y.N.,
Kim, B.,
Wearable Mobile-Based Emotional Response-Monitoring System for
Drivers,
HMS(47), No. 5, October 2017, pp. 636-649.
IEEE DOI
1709
Accidents, Electromyography, Fatigue, Monitoring, Sensors, Stress,
Vehicles, Healthcare, mobile application, negative emotion,
roadway accident, stress, wearable, system
BibRef
Wang, Z.,
Zheng, R.,
Kaizuka, T.,
Shimono, K.,
Nakano, K.,
The Effect of a Haptic Guidance Steering System on Fatigue-Related
Driver Behavior,
HMS(47), No. 5, October 2017, pp. 741-748.
IEEE DOI
1709
Fatigue, Haptic interfaces, Steering systems, Torque, Trajectory,
Vehicles, Wheels, Driver behavior, driving simulator,
haptic guidance, passive, fatigue
BibRef
Sun, W.,
Zhang, X.,
Peeta, S.,
He, X.,
Li, Y.,
A Real-Time Fatigue Driving Recognition Method Incorporating
Contextual Features and Two Fusion Levels,
ITS(18), No. 12, December 2017, pp. 3408-3420.
IEEE DOI
1712
Brain modeling, Computational modeling, Fatigue, Real-time systems,
Reliability, Sleep, Vehicles, Dempster-Shafer evidence theory,
multi-class support vector machine classifier
BibRef
Yan, R.H.[Rong-Hui],
Wu, C.[Cheng],
Wang, Y.M.[Yi-Ming],
Exploration and evaluation of individual difference to driving fatigue
for high-speed railway: a parametric SVM model based on
multidimensional visual cue,
IET-ITS(12), No. 6, August 2018, pp. 504-512.
DOI Link
1807
BibRef
Wang, P.[Ping],
Min, J.L.[Jian-Liang],
Hu, J.F.[Jian-Feng],
Ensemble classifier for driver's fatigue detection based on a single
EEG channel,
IET-ITS(12), No. 10, December 2018, pp. 1322-1328.
DOI Link
1812
BibRef
Gu, W.H.[Wang Huan],
Zhu, Y.[Yu],
Chen, X.D.[Xu Dong],
He, L.F.[Lin Fei],
Zheng, B.B.[Bing Bing],
Hierarchical CNN-based real-time fatigue detection system by
visual-based technologies using MSP model,
IET-IPR(12), No. 12, December 2018, pp. 2319-2329.
DOI Link
1812
BibRef
Vo, S.A.[Son Anh],
Mirowski, L.[Luke],
Scanlan, J.[Joel],
Turner, P.[Paul],
Verifying driver performance for heavy haulage fatigue management,
IET-ITS(13), No. 6, June 2019, pp. 1033-1040.
DOI Link
1906
BibRef
Sikander, G.,
Anwar, S.,
Driver Fatigue Detection Systems: A Review,
ITS(20), No. 6, June 2019, pp. 2339-2352.
IEEE DOI
1906
Survey, Driver Fatigue. Fatigue, Vehicles, Sleep, Monitoring, Mathematical model,
Feature extraction, Task analysis, Intelligent transportation,
driver monitoring
BibRef
Amodio, A.,
Ermidoro, M.,
Maggi, D.,
Formentin, S.,
Savaresi, S.M.,
Automatic Detection of Driver Impairment Based on Pupillary Light
Reflex,
ITS(20), No. 8, August 2019, pp. 3038-3048.
IEEE DOI
1908
Vehicles, Fatigue, Video sequences, Sleep, Roads, Monitoring, Cameras,
ADAS, system identification, video processing, pupil dynamics,
support vector machine
BibRef
Li, X.[Xu],
Hong, L.[Lin],
Wang, J.C.[Jian-Chun],
Liu, X.[Xiang],
Fatigue driving detection model based on multi-feature fusion and
semi-supervised active learning,
IET-ITS(13), No. 9, September 2019, pp. 1401-1409.
DOI Link
1908
BibRef
Xiao, Z.T.[Zhi-Tao],
Hu, Z.Q.A.[Zhi-Qi-Ang],
Geng, L.[Lei],
Zhang, F.[Fang],
Wu, J.[Jun],
Li, Y.L.[Yue-Long],
Fatigue driving recognition network: fatigue driving recognition via
convolutional neural network and long short-term memory units,
IET-ITS(13), No. 9, September 2019, pp. 1410-1416.
DOI Link
1908
BibRef
Dasgupta, A.,
Rahman, D.,
Routray, A.,
A Smartphone-Based Drowsiness Detection and Warning System for
Automotive Drivers,
ITS(20), No. 11, November 2019, pp. 4045-4054.
IEEE DOI
1911
Vehicles, Face, Cameras, Lighting, Automotive engineering, Fatigue,
Sensors, PERCLOS, voiced-unvoiced ratio, Android, drowsiness
BibRef
Chaudhuri, A.,
Routray, A.,
Driver Fatigue Detection Through Chaotic Entropy Analysis of Cortical
Sources Obtained From Scalp EEG Signals,
ITS(21), No. 1, January 2020, pp. 185-198.
IEEE DOI
2001
Electroencephalography, Sleep, Fatigue, Entropy, Scalp, Brain modeling,
Vehicles, Chaotic entropy, dipoles, scalp EEG, sleep deprivation,
support vector machines
BibRef
Kurosawa, Y.[Yuki],
Mochiduki, S.[Shinya],
Hoshino, Y.[Yuko],
Yamada, M.[Mitsuho],
Measurement of Fatigue Based on Changes in Eye Movement during Gaze,
IEICE(E103-D), No. 5, May 2020, pp. 1203-1207.
WWW Link.
2005
BibRef
Liu, Z.M.[Zhong-Min],
Peng, Y.X.[Yu-Xi],
Hu, W.J.[Wen-Jin],
Driver fatigue detection based on deeply-learned facial expression
representation,
JVCIR(71), 2020, pp. 102723.
Elsevier DOI
2009
Fatigue detection, MB-LBP, PERCLOS, Fuzzy reasoning
BibRef
Li, F.,
Chen, C.H.,
Xu, G.,
Khoo, L.P.,
Hierarchical Eye-Tracking Data Analytics for Human Fatigue Detection
at a Traffic Control Center,
HMS(50), No. 5, October 2020, pp. 465-474.
IEEE DOI
2009
Fatigue, Interpolation, Data integrity, Noise measurement,
Data mining, Tracking, Data analysis, Eye movement,
traffic management
BibRef
Nemcová, A.[Andrea],
Svozilová, V.[Veronika],
Bucsuházy, K.[Katerina],
Smíšek, R.[Radovan],
Mézl, M.[Martin],
Hesko, B.[Branislav],
Belák, M.[Michal],
Bilík, M.[Martin],
Maxera, P.[Pavel],
Seitl, M.[Martin],
Dominik, T.[Tomáš],
Semela, M.[Marek],
Šucha, M.[Matúš],
Kolár, R.[Radim],
Multimodal Features for Detection of Driver Stress and Fatigue:
Review,
ITS(22), No. 6, June 2021, pp. 3214-3233.
IEEE DOI
2106
Survey, Driver Monitoring. Stress, Fatigue, Databases, Accidents, Automobiles, Sleep,
Driver fatigue, driver stress, traffic accident,
multimodal features
BibRef
Zhao, Q.J.[Qi-Jie],
Jiang, J.[Junye],
Lei, Z.G.[Zhi-Gao],
Yi, J.G.[Jin-Gang],
Detection method of eyes opening and closing ratio for driver's
fatigue monitoring,
IET-ITS(15), No. 1, 2021, pp. 31-42.
DOI Link
2106
BibRef
Sun, Y.F.[Yi-Fan],
Wu, C.Z.[Chao-Zhong],
Zhang, H.[Hui],
Zhou, W.[Wei],
Li, X.[Xin],
Zhang, Q.[Qi],
Extraction of optimal fatigue-driving steering indicators considering
individual differences,
IET-ITS(15), No. 5, 2021, pp. 606-618.
DOI Link
2106
BibRef
Liu, M.Z.[Ming-Zhou],
Xu, X.[Xin],
Hu, J.[Jing],
Jiang, Q.N.[Qian-Nan],
Real time detection of driver fatigue based on CNN-LSTM,
IET-IPR(16), No. 2, 2022, pp. 576-595.
DOI Link
2201
BibRef
Zhao, G.Z.[Guang-Zhe],
He, Y.Q.[Yan-Qing],
Yang, H.[Hanting],
Tao, Y.[Yong],
Research on fatigue detection based on visual features,
IET-IPR(16), No. 4, 2022, pp. 1044-1053.
DOI Link
2203
BibRef
Yang, Y.X.[Yu-Xuan],
Gao, Z.K.[Zhong-Ke],
Li, Y.L.[Yan-Li],
Cai, Q.[Qing],
Marwan, N.[Norbert],
Kurths, J.[Jürgen],
A Complex Network-Based Broad Learning System for Detecting Driver
Fatigue From EEG Signals,
SMCS(51), No. 9, September 2021, pp. 5800-5808.
IEEE DOI
2108
Fatigue, Electroencephalography, Vehicles, Learning systems, Indexes,
Biomedical monitoring, Task analysis,
electroencephalogram (EEG) signals
BibRef
Huang, R.[Rui],
Wang, Y.[Yan],
Li, Z.J.[Zi-Jian],
Lei, Z.[Zeyu],
Xu, Y.F.[Yu-Fan],
RF-DCM: Multi-Granularity Deep Convolutional Model Based on Feature
Recalibration and Fusion for Driver Fatigue Detection,
ITS(23), No. 1, January 2022, pp. 630-640.
IEEE DOI
2201
Fatigue, Feature extraction, Face, Vehicles, Time-domain analysis,
Fuses, Videos, Deep learning, fatigue detection,
multi-granularity network
BibRef
Wu, E.Q.[Edmond Q.],
Zhou, M.C.[Meng-Chu],
Hu, D.[Dewen],
Zhu, L.J.[Long-Jun],
Tang, Z.R.[Zhi-Ri],
Qiu, X.Y.[Xu-Yi],
Deng, P.Y.[Ping-Yu],
Zhu, L.M.[Li-Min],
Ren, H.[He],
Self-Paced Dynamic Infinite Mixture Model for Fatigue Evaluation of
Pilots' Brains,
Cyber(52), No. 7, July 2022, pp. 5623-5638.
IEEE DOI
2207
Electroencephalography, Fatigue, Brain modeling, Mixture models,
Task analysis, Regulation, Load modeling, Brain fatigue,
self-paced learning
BibRef
Wu, E.Q.[Edmond Q.],
Zhou, M.C.[Meng-Chu],
Xiong, P.W.[Peng-Wen],
Tang, Z.R.[Zhi-Ri],
Hu, R.H.[Rui-Han],
Jie, Y.W.[Yu-Wen],
Inferring Flight Performance Under Different Maneuvers With Pilot's
Multi-Physiological Parameters,
ITS(23), No. 8, August 2022, pp. 11338-11348.
IEEE DOI
2208
Gaussian processes, Atmospheric modeling, Task analysis,
Brain modeling, Pupils, Heart rate variability, Physiology, workload
BibRef
Wu, E.Q.[Edmond Q.],
Cao, Z.T.[Zheng-Tao],
Sun, P.Z.H.[Poly Z. H.],
Li, D.F.[Dong-Fang],
Law, R.[Rob],
Xu, X.[Xin],
Zhu, L.M.[Li-Min],
Yu, M.[Mengsun],
Inferring Cognitive State of Pilot's Brain Under Different Maneuvers
During Flight,
ITS(23), No. 11, November 2022, pp. 21729-21739.
IEEE DOI
2212
Fatigue, Brain modeling, Electroencephalography,
Feature extraction, Generative adversarial networks, Electrodes,
brain power map
BibRef
Ahlström, C.[Christer],
van Leeuwen, W.[Wessel],
Krupenia, S.[Stas],
Jansson, H.[Herman],
Finér, S.[Svitlana],
Anund, A.[Anna],
Kecklund, G.[Göran],
Real-Time Adaptation of Driving Time and Rest Periods in Automated
Long-Haul Trucking: Development of a System Based on Biomathematical
Modelling, Fatigue and Relaxation Monitoring,
ITS(23), No. 5, May 2022, pp. 4758-4766.
IEEE DOI
2205
Sleep, Vehicles, Fatigue, Heart rate variability, Regulation,
Automobiles, Real-time systems, Hours of service regulations, truck
BibRef
Wu, E.Q.[Edmond Q.],
Zhu, L.M.[Li-Min],
Li, G.J.[Gui-Jiang],
Li, H.J.[Hong-Jun],
Tang, Z.[Zhiri],
Hu, R.[Ruihan],
Zhou, G.R.[Gui-Rong],
Nonparametric Hierarchical Hidden Semi-Markov Model for Brain Fatigue
Behavior Detection of Pilots During Flight,
ITS(23), No. 6, June 2022, pp. 5245-5256.
IEEE DOI
2206
Hidden Markov models, Brain modeling, Fatigue, Mixture models,
Electroencephalography, Vehicles, Feature extraction,
t-mixture model
BibRef
Horberry, T.[Tim],
Mulvihill, C.[Christine],
Fitzharris, M.[Michael],
Lawrence, B.[Brendan],
Lenné, M.[Mike],
Kuo, J.[Jonny],
Wood, D.[Darren],
Human-Centered Design for an In-Vehicle Truck Driver Fatigue and
Distraction Warning System,
ITS(23), No. 6, June 2022, pp. 5350-5359.
IEEE DOI
2206
Vehicles, Fatigue, Alarm systems, Visualization, Safety, Task analysis,
Australia, Human centered design, driver fatigue,
driver warning systems
BibRef
Fan, C.J.[Chao-Jie],
Peng, Y.[Yong],
Peng, S.L.[Shuang-Ling],
Zhang, H.H.[Hong-Hao],
Wu, Y.K.[Yuan-Kai],
Kwong, S.[Sam],
Detection of Train Driver Fatigue and Distraction Based on Forehead
EEG: A Time-Series Ensemble Learning Method,
ITS(23), No. 8, August 2022, pp. 13559-13569.
IEEE DOI
2208
Fatigue, Electroencephalography, Vehicles, Task analysis,
Rail transportation, Forehead, Feature extraction, Train driver,
time-series ensemble learning method
BibRef
Ansari, S.[Shahzeb],
Naghdy, F.[Fazel],
Du, H.P.[Hai-Ping],
Pahnwar, Y.N.[Yasmeen Naz],
Driver Mental Fatigue Detection Based on Head Posture Using New
Modified reLU-BiLSTM Deep Neural Network,
ITS(23), No. 8, August 2022, pp. 10957-10969.
IEEE DOI
2208
Vehicles, Fatigue, Magnetic heads, Monitoring, Biomedical monitoring,
Tools, Vehicle dynamics, Driver behaviour,
reLU-BiLSTM
BibRef
Lv, C.[Chao],
Nian, J.T.[Jin-Tao],
Xu, Y.[Yaru],
Song, B.[Bo],
Compact Vehicle Driver Fatigue Recognition Technology Based on EEG
Signal,
ITS(23), No. 10, October 2022, pp. 19753-19759.
IEEE DOI
2210
Fatigue, Electroencephalography, Vehicles, Wavelet packets,
Brain modeling, Clustering algorithms, Software,
convolutional neural network
BibRef
Wu, E.Q.[Edmond Q.],
Lin, C.T.[Chin-Teng],
Zhu, L.M.[Li-Min],
Tang, Z.R.,
Jie, Y.W.[Yu-Wen],
Zhou, G.R.[Gui-Rong],
Fatigue Detection of Pilots' Brain Through Brains Cognitive Map and
Multilayer Latent Incremental Learning Model,
Cyber(52), No. 11, November 2022, pp. 12302-12314.
IEEE DOI
2211
Fatigue, Brain modeling, Rhythm, Electroencephalography,
Market research, Electrodes, Adaptation models, Brain power map,
nonparametric prior
BibRef
Du, G.L.[Guang-Long],
Zhang, L.L.[Lin-Lin],
Su, K.[Kang],
Wang, X.Q.[Xue-Qian],
Teng, S.H.[Shao-Hua],
Liu, P.X.[Peter X.],
A Multimodal Fusion Fatigue Driving Detection Method Based on Heart
Rate and PERCLOS,
ITS(23), No. 11, November 2022, pp. 21810-21820.
IEEE DOI
2212
Fatigue, Feature extraction, Iris, Heart rate, Brain modeling, Eyelids,
Vehicles, Multimodal feature fusion, fatigue driving detection,
heart rate
BibRef
Bakker, B.[Bram],
Zablocki, B.[Bartosz],
Baker, A.[Angela],
Riethmeister, V.[Vanessa],
Marx, B.[Bernd],
Iyer, G.[Girish],
Anund, A.[Anna],
Ahlström, C.[Christer],
A Multi-Stage, Multi-Feature Machine Learning Approach to Detect
Driver Sleepiness in Naturalistic Road Driving Conditions,
ITS(23), No. 5, May 2022, pp. 4791-4800.
IEEE DOI
2205
Sleep, Vehicles, Fatigue, Feature extraction, Roads,
Hidden Markov models, Faces, Fatigue detection, video-based,
field trial
BibRef
Zuo, X.[Xin],
Zhang, C.[Chi],
Cong, F.Y.[Feng-Yu],
Zhao, J.[Jian],
Hämäläinen, T.[Timo],
Driver Distraction Detection Using Bidirectional Long Short-Term
Network Based on Multiscale Entropy of EEG,
ITS(23), No. 10, October 2022, pp. 19309-19322.
IEEE DOI
2210
Vehicles, Electroencephalography, Feature extraction,
Task analysis, Entropy, Fatigue, Complexity theory,
BiLSTM
BibRef
Zhang, Z.M.[Zhi-Min],
Ning, H.S.[Huan-Sheng],
Zhou, F.[Fang],
A Systematic Survey of Driving Fatigue Monitoring,
ITS(23), No. 11, November 2022, pp. 19999-20020.
IEEE DOI
2212
Fatigue, Monitoring, Vehicles, Sleep, Transportation, Task analysis,
Biomedical monitoring, Driving fatigue monitoring, fatigue assessment
BibRef
Yang, C.[Cong],
Yang, Z.Y.[Zhen-Yu],
Li, W.Y.[Wei-Yu],
See, J.[John],
FatigueView:
A Multi-Camera Video Dataset for Vision-Based Drowsiness Detection,
ITS(24), No. 1, January 2023, pp. 233-246.
IEEE DOI
2301
Visualization, Cameras, Mouth, Vehicles, Annotations, Training data,
Intelligent transportation systems, Drowsiness detection, autonomous vehicles
BibRef
Zhou, Y.[Yi],
Zeng, C.[ChangQing],
Mu, Z.[ZhenDong],
Optimal feature-algorithm combination research for EEG fatigue
driving detection based on functional brain network,
IET-Bio(12), No. 2, 2023, pp. 65-76.
DOI Link
2305
brain-computer interfaces, feature extraction
BibRef
Fa, S.X.[Shu-Xiang],
Yang, X.H.[Xiao-Hui],
Han, S.Y.[Shi-Yuan],
Feng, Z.Q.[Zhi-Quan],
Chen, Y.H.[Yue-Hui],
Multi-scale spatial-temporal attention graph convolutional networks
for driver fatigue detection,
JVCIR(93), 2023, pp. 103826.
Elsevier DOI
2305
Driver fatigue detection, Driver behavior,
Deep learning, Graph convolution networks
BibRef
Du, G.L.[Guang-Long],
Long, S.Y.[Shuai-Ying],
Li, C.Q.[Chun-Quan],
Wang, Z.Y.[Zhi-Yao],
Liu, P.X.[Peter X.],
A Product Fuzzy Convolutional Network for Detecting Driving Fatigue,
Cyber(53), No. 7, July 2023, pp. 4175-4188.
IEEE DOI
2307
Electroencephalography, Fatigue, Electrocardiography,
Feature extraction, Brain modeling, Fuzzy neural networks,
product fuzzy convolutional network (PFCN)
BibRef
Liu, S.G.[Shu-Gang],
Wang, Y.J.[Yu-Jie],
Yu, Q.G.[Qiang-Guo],
Zhan, J.[Jie],
Liu, H.L.[Hong-Li],
Liu, J.T.[Jiang-Tao],
A Driver Fatigue Detection Algorithm Based on Dynamic Tracking of Small
Facial Targets Using YOLOv7,
IEICE(E106-D), No. 11, November 2023, pp. 1881-1890.
WWW Link.
2311
BibRef
Gu, Y.[Yi],
Xia, K.J.[Kai-Jian],
Lai, K.W.[Khin-Wee],
Jiang, Y.Z.[Yi-Zhang],
Qian, P.J.[Peng-Jiang],
Gu, X.Q.[Xiao-Qing],
Transferable Takagi-Sugeno-Kang Fuzzy Classifier With Multi-Views for
EEG-Based Driving Fatigue Recognition in Intelligent Transportation,
ITS(24), No. 12, December 2023, pp. 15807-15817.
IEEE DOI
2312
BibRef
Xu, T.[Tao],
Wang, H.T.[Hong-Tao],
Lu, G.[Guanyong],
Wan, F.[Feng],
Deng, M.Q.[Meng-Qi],
Qi, P.[Peng],
Bezerianos, A.[Anastasios],
Guan, C.T.[Cun-Tai],
Sun, Y.[Yu],
E-Key: An EEG-Based Biometric Authentication and Driving Fatigue
Detection System,
AffCom(14), No. 2, April 2023, pp. 864-877.
IEEE DOI
2306
Fatigue, Electroencephalography, Brain modeling,
Convolutional neural networks, Feature extraction, Data models, biometric
BibRef
Gu, Y.[Yi],
Jiang, Y.Z.[Yi-Zhang],
Wang, T.T.[Ting-Ting],
Qian, P.J.[Peng-Jiang],
Gu, X.Q.[Xiao-Qing],
EEG-Based Driver Mental Fatigue Recognition in COVID-19 Scenario
Using a Semi-Supervised Multi-View Embedding Learning Model,
ITS(25), No. 1, January 2024, pp. 859-868.
IEEE DOI
2402
Electroencephalography, Fatigue, Brain modeling, Vehicles, Kernel,
Sparse matrices, Feature extraction, kernel trick
BibRef
Laouz, H.,
Ayad, S.,
Terrissa, L.S.,
Literature Review on Driver's Drowsiness and Fatigue Detection,
ISCV20(1-7)
IEEE DOI
2011
electrocardiography, electroencephalography, electromyography,
electro-oculography, human factors, medical signal processing,
Physiological and Behavioral Measures
BibRef
Zhang, F.,
Su, J.,
Geng, L.,
Xiao, Z.,
Driver Fatigue Detection Based on Eye State Recognition,
CMVIT17(105-110)
IEEE DOI
1704
feedforward neural nets
BibRef
Yin, H.,
Su, Y.,
Liu, Y.,
Zhao, D.,
A driver fatigue detection method based on multi-sensor signals,
WACV16(1-7)
IEEE DOI
1606
Cameras
BibRef
Li, Z.[Zibo],
Zhang, F.[Fan],
Sun, G.M.[Guang-Min],
Zhao, D.Q.[De-Qun],
Zheng, K.[Kun],
Driver Fatigue Detection System Based on DSP Platform,
MMMod16(II: 47-53).
Springer DOI
1601
BibRef
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Sawicki, D.[Dariusz],
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Chapter on Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities continues in
License Plate Recognition, Extraction, Analysis .