drowsiness_detection.md

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Click on each entry below to see additional information.

Lu et al., JHPFA-Net: Joint Head Pose and Facial Action Network for Driver Yawning Detection Across Arbitrary Poses in Videos, Trans. ITS, 2023 | paper

Dataset(s): YawDD

@article{2023_T-ITS_Lu,
    author = "Lu, Yansha and Liu, Chunsheng and Chang, Faliang and Liu, Hui and Huan, Hengqiang",
    journal = "IEEE Transactions on Intelligent Transportation Systems",
    publisher = "IEEE",
    title = "JHPFA-Net: Joint Head Pose and Facial Action Network for Driver Yawning Detection Across Arbitrary Poses in Videos",
    year = "2023"
}

Chen et al., A Multi-view Driver Drowsiness Detection Method Using Transfer Learning and Population-based Sampling Strategy, ITSC, 2022 | paper

Dataset(s): private

@inproceedings{2022_ITSC_Chen,
    author = "Chen, Jinxin and Fang, Zhenwu and Wang, Jinxiang and Chen, Jiansong and Yin, Guodong",
    booktitle = "2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)",
    organization = "IEEE",
    pages = "3386--3391",
    title = "A Multi-view Driver Drowsiness Detection Method Using Transfer Learning and Population-based Sampling Strategy",
    year = "2022"
}

Baccour et al., Comparative Analysis of Vehicle-Based and Driver-Based Features for Driver Drowsiness Monitoring by Support Vector Machines, Trans. ITS, 2022 | paper

Dataset(s): private

@article{2022_T-ITS_Baccour,
    author = {Baccour, Mohamed Hedi and Driewer, Frauke and Sch{\"a}ck, Tim and Kasneci, Enkelejda},
    journal = "IEEE Transactions on Intelligent Transportation Systems",
    number = "12",
    pages = "23164--23178",
    publisher = "IEEE",
    title = "Comparative Analysis of Vehicle-Based and Driver-Based Features for Driver Drowsiness Monitoring by Support Vector Machines",
    volume = "23",
    year = "2022"
}

Luo et al., Detecting Driver Cognition Alertness State From Visual Activities in Normal and Emergency Scenarios, Trans. ITS, 2022 | paper

Dataset(s): private

@article{2022_T-ITS_Luo,
    author = "Luo, Longxi and Wu, Jianping and Fei, Weijie and Bi, Luzheng and Fan, Xinan",
    journal = "IEEE Transactions on Intelligent Transportation Systems",
    number = "10",
    pages = "19497--19510",
    publisher = "IEEE",
    title = "Detecting Driver Cognition Alertness State From Visual Activities in Normal and Emergency Scenarios",
    volume = "23",
    year = "2022"
}

Bakker et al., A Multi-Stage, Multi-Feature Machine Learning Approach to Detect Driver Sleepiness in Naturalistic Road Driving Conditions, Trans. ITS, 2022 | paper

Dataset(s): private

@article{2022_T-ITS_Bakker,
    author = {Bakker, Bram and Zab{\l}ocki, Bartosz and Baker, Angela and Riethmeister, Vanessa and Marx, Bernd and Iyer, Girish and Anund, Anna and Ahlstr{\"o}m, Christer},
    journal = "IEEE Transactions on Intelligent Transportation Systems",
    number = "5",
    pages = "4791--4800",
    publisher = "IEEE",
    title = "A multi-stage, multi-feature machine learning approach to detect driver sleepiness in naturalistic road driving conditions",
    volume = "23",
    year = "2021"
}

Lollett et al., Driver’s Drowsiness Classifier using a Single-Camera Robust to Mask-wearing Situations using an Eyelid, Lower-Face Contour, and Chest Movement Feature Vector GRU-based Model, IV, 2022 | paper

Dataset(s): private

@inproceedings{2022_IV_Lollett,
    author = "Lollett, Catherine and Kamezaki, Mitsuhiro and Sugano, Shigeki",
    booktitle = "2022 IEEE Intelligent Vehicles Symposium (IV)",
    organization = "IEEE",
    pages = "519--526",
    title = "Driver’s Drowsiness Classifier using a Single-Camera Robust to Mask-wearing Situations using an Eyelid, Lower-Face Contour, and Chest Movement Feature Vector GRU-based Model",
    year = "2022"
}

Tufekci et al., Detecting Driver Drowsiness as an Anomaly Using LSTM Autoencoders, ECCVW, 2022 | paper

Dataset(s): DDD

@inproceedings{2022_ECCVW_Tufekci,
    author = {T{\"u}fekci, G{\"u}lin and Kayaba{\c{s}}{\i}, Alper and Akag{\"u}nd{\"u}z, Erdem and Ulusoy, {\.I}lkay},
    booktitle = "Computer Vision--ECCV 2022 Workshops: Tel Aviv, Israel, October 23--27, 2022, Proceedings, Part VI",
    organization = "Springer",
    pages = "549--559",
    title = "Detecting Driver Drowsiness as an Anomaly Using LSTM Autoencoders",
    year = "2023"
}

Sharak et al., Contact Versus Noncontact Detection of Driver’s Drowsiness, ICPR, 2022 | paper

Dataset(s): private

@inproceedings{2022_ICPR_Sharak,
    author = "Sharak, Salem and Das, Kapotaksha and Riani, Kais and Abouelenien, Mohamed and Burzo, Mihai and Mihalcea, Rada",
    booktitle = "2022 26th International Conference on Pattern Recognition (ICPR)",
    organization = "IEEE",
    pages = "967--974",
    title = "Contact Versus Noncontact Detection of Driver’s Drowsiness",
    year = "2022"
}

Du et al., A Multimodal Fusion Fatigue Driving Detection Method Based on Heart Rate and PERCLOS, Trans. ITS, 2021 | paper

Dataset(s): RLDD

@article{2021_T-ITS_Du,
    author = "Du, Guanglong and Zhang, Linlin and Su, Kang and Wang, Xueqian and Teng, Shaohua and Liu, Peter X",
    journal = "Ieee Transactions on Intelligent Transportation Systems",
    number = "11",
    pages = "21810--21820",
    publisher = "IEEE",
    title = "A multimodal fusion fatigue driving detection method based on heart rate and PERCLOS",
    volume = "23",
    year = "2022"
}

Bakker et al., A Multi-Stage, Multi-Feature Machine Learning Approach to Detect Driver Sleepiness in Naturalistic Road Driving Conditions, Trans. ITS, 2021 | paper

Dataset(s): private

@article{2021_T-ITS_Bakker,
    author = {Bakker, Bram and Zab{\l}ocki, Bartosz and Baker, Angela and Riethmeister, Vanessa and Marx, Bernd and Iyer, Girish and Anund, Anna and Ahlstr{\"o}m, Christer},
    journal = "IEEE Transactions on Intelligent Transportation Systems",
    title = "A multi-stage, multi-feature machine learning approach to detect driver sleepiness in naturalistic road driving conditions",
    year = "2021"
}

Ansari et al., Driver Mental Fatigue Detection Based on Head Posture Using New Modified reLU-BiLSTM Deep Neural Network, Trans. ITS, 2021 | paper

Dataset(s): private

@article{2021_T-ITS_Ansari,
    author = "Ansari, Shahzeb and Naghdy, Fazel and Du, Haiping and Pahnwar, Yasmeen Naz",
    journal = "IEEE Transactions on Intelligent Transportation Systems",
    number = "8",
    pages = "10957--10969",
    publisher = "IEEE",
    title = "Driver mental fatigue detection based on head posture using new modified reLU-BiLSTM deep neural network",
    volume = "23",
    year = "2021"
}

Ahmed et al., Intelligent Driver Drowsiness Detection for Traffic Safety Based on Multi CNN Deep Model and Facial Subsampling, Trans. ITS, 2021 | paper

Dataset(s): DDD

@article{2021_T-ITS_Ahmed,
    author = "Ahmed, Muneeb and Masood, Sarfaraz and Ahmad, Musheer and Abd El-Latif, Ahmed A",
    journal = "IEEE Transactions on Intelligent Transportation Systems",
    number = "10",
    pages = "19743--19752",
    publisher = "IEEE",
    title = "Intelligent driver drowsiness detection for traffic safety based on multi CNN deep model and facial subsampling",
    volume = "23",
    year = "2021"
}

Huang et al., RF-DCM: Multi-Granularity Deep Convolutional Model Based on Feature Recalibration and Fusion for Driver Fatigue Detection, Trans. ITS, 2020 | paper

Dataset(s): DDD

@article{2020_T-ITS_Huang,
    author = "Huang, Rui and Wang, Yan and Li, Zijian and Lei, Zeyu and Xu, Yufan",
    journal = "IEEE Transactions on Intelligent Transportation Systems",
    title = "RF-DCM: Multi-Granularity Deep Convolutional Model Based on Feature Recalibration and Fusion for Driver Fatigue Detection",
    year = "2020"
}

Joshi et al., In-the-wild Drowsiness Detection from Facial Expressions, IV, 2020 | paper

Dataset(s): private

@inproceedings{2020_IV_Joshi,
    author = "Joshi, Ajjen and Kyal, Survi and Banerjee, Sandipan and Mishra, Taniya",
    booktitle = "IV",
    title = "In-the-wild drowsiness detection from facial expressions",
    year = "2020"
}

Dari et al., Unsupervised Blink Detection and Driver Drowsiness Metrics on Naturalistic Driving Data, ITSC, 2020 | paper

Dataset(s): private

@inproceedings{2020_ITSC_Dari,
    author = "Dari, Simone and Epple, Nico and Protschky, Valentin",
    booktitle = "ITSC",
    title = "Unsupervised Blink Detection and Driver Drowsiness Metrics on Naturalistic Driving Data",
    year = "2020"
}

Tran et al., Real-time Detection of Distracted Driving using Dual Cameras, IROS, 2020 | paper

Dataset(s): private

@inproceedings{2020_IROS_Tran,
    author = "Tran, Duy and Do, Ha Manh and Lu, Jiaxing and Sheng, Weihua",
    booktitle = "IROS",
    title = "Real-time Detection of Distracted Driving using Dual Cameras",
    year = "2020"
}

Vijay et al., Real-Time Driver Drowsiness Detection using Facial Action Units, ICPR, 2020 | paper

Dataset(s): DDD

@inproceedings{2020_ICPR_Vijay,
    author = "Vijay, Malaika and Vinayak, Nandagopal Netrakanti and Nunna, Maanvi and Natarajan, Subramanyam",
    booktitle = "ICPR",
    title = "Real-Time Driver Drowsiness Detection using Facial Action Units",
    year = "2021"
}

Chiou et al., Driver Monitoring Using Sparse Representation With Part-Based Temporal Face Descriptors, Trans. ITS, 2019 | paper

Dataset(s): YawDD, DDD

@article{2019_T-ITS_Chiou,
    author = "Chiou, Chien-Yu and Wang, Wei-Cheng and Lu, Shueh-Chou and Huang, Chun-Rong and Chung, Pau-Choo and Lai, Yun-Yang",
    journal = "IEEE Transactions on Intelligent Transportation Systems",
    number = "1",
    pages = "346--361",
    publisher = "IEEE",
    title = "Driver monitoring using sparse representation with part-based temporal face descriptors",
    volume = "21",
    year = "2019"
}

Wang et al., Eye gaze pattern analysis for fatigue detection based on GP-BCNN with ESM, Pattern Recognition Letters, 2019 | paper

Dataset(s): private

@article{2019_PRL_Wang,
    author = "Wang, Yan and Huang, Rui and Guo, Lei",
    journal = "Pattern Recognition Letters",
    pages = "61--74",
    publisher = "Elsevier",
    title = "Eye gaze pattern analysis for fatigue detection based on GP-BCNN with ESM",
    volume = "123",
    year = "2019"
}

Zhang et al., Driver Drowsiness Detection using Multi-Channel Second Order Blind Identifications, IEEE Access, 2019 | paper

Dataset(s): private

@article{2019_IEEEAccess_Zhang,
    author = "Zhang, Chao and Wu, Xiaopei and Zheng, Xi and Yu, Shui",
    journal = "IEEE Access",
    pages = "11829--11843",
    publisher = "IEEE",
    title = "Driver drowsiness detection using multi-channel second order blind identifications",
    volume = "7",
    year = "2019"
}

Deng et al., Real-Time Driver-Drowsiness Detection System Using Facial Features, IEEE Access, 2019 | paper

Dataset(s): private

@article{2019_IEEEAccess_Deng,
    author = "Deng, Wanghua and Wu, Ruoxue",
    journal = "IEEE Access",
    pages = "118727--118738",
    publisher = "IEEE",
    title = "Real-time driver-drowsiness detection system using facial features",
    volume = "7",
    year = "2019"
}

Ghoddoosian et al., A Realistic Dataset and Baseline Temporal Model for Early Drowsiness Detection, CVPRW, 2019 | paper

Dataset(s): RLDD

@inproceedings{2019_CVPRW_Ghoddoosian,
    author = "Ghoddoosian, Reza and Galib, Marnim and Athitsos, Vassilis",
    booktitle = "CVPRW",
    title = "A realistic dataset and baseline temporal model for early drowsiness detection",
    year = "2019"
}

Yu et al., Driver Drowsiness Detection Using Condition-Adaptive Representation Learning Framework, Trans. ITS, 2018 | paper

Dataset(s): DDD

@article{2018_T-ITS_Yu,
    author = "Yu, Jongmin and Park, Sangwoo and Lee, Sangwook and Jeon, Moongu",
    journal = "IEEE Transactions on Intelligent Transportation Systems",
    number = "11",
    pages = "4206--4218",
    title = "Driver drowsiness detection using condition-adaptive representation learning framework",
    volume = "20",
    year = "2018"
}

Dasgupta et al., A Smartphone-Based Drowsiness Detection and Warning System for Automotive Drivers, Trans. ITS, 2018 | paper

Dataset(s): private

@article{2018_T-ITS_Dasgupta,
    author = "Dasgupta, Anirban and Rahman, Daleef and Routray, Aurobinda",
    journal = "IEEE Transactions on Intelligent Transportation Systems",
    number = "11",
    pages = "4045--4054",
    title = "A smartphone-based drowsiness detection and warning system for automotive drivers",
    volume = "20",
    year = "2018"
}

Gwak et al., Early Detection of Driver Drowsiness Utilizing Machine Learning based on Physiological Signals, Behavioral Measures, and Driving Performance, ITSC, 2018 | paper

Dataset(s): private

@inproceedings{2018_ITSC_Gwak,
    author = "Gwak, Jongseong and Shino, Motoki and Hirao, Akinari",
    booktitle = "ITSC",
    title = "Early detection of driver drowsiness utilizing machine learning based on physiological signals, behavioral measures, and driving performance",
    year = "2018"
}

Sun et al., A Real-Time Fatigue Driving Recognition Method Incorporating Contextual Features and Two Fusion Levels, Trans. ITS, 2017 | paper

Dataset(s): private

@article{2017_T-ITS_Sun,
    author = "Sun, Wei and Zhang, Xiaorui and Peeta, Srinivas and He, Xiaozheng and Li, Yongfu",
    journal = "IEEE Transactions on Intelligent Transportation Systems",
    number = "12",
    pages = "3408--3420",
    title = "A real-time fatigue driving recognition method incorporating contextual features and two fusion levels",
    volume = "18",
    year = "2017"
}

Zhao et al., Driver drowsiness detection using facial dynamic fusion information and a DBN, IET Intelligent Transport Systems, 2017 | paper

Dataset(s): private

@article{2017_IET_Zhao,
    author = "Zhao, Lei and Wang, Zengcai and Wang, Xiaojin and Liu, Qing",
    journal = "IET Intelligent Transport Systems",
    number = "2",
    pages = "127--133",
    title = "Driver drowsiness detection using facial dynamic fusion information and a DBN",
    volume = "12",
    year = "2017"
}

Reddy et al., Real-time Driver Drowsiness Detection for Embedded System Using Model Compression of Deep Neural Networks, CVPRW, 2017 | paper

Dataset(s): private

@inproceedings{2017_CVPRW_Reddy,
    author = "Reddy, Bhargava and Kim, Ye-Hoon and Yun, Sojung and Seo, Chanwon and Jang, Junik",
    booktitle = "CVPRW",
    title = "Real-time driver drowsiness detection for embedded system using model compression of deep neural networks",
    year = "2017"
}

Yu et al., Representation Learning, Scene Understanding, and Feature Fusion for Drowsiness Detection, ACCVW, 2017 | paper

Dataset(s): DDD

@inproceedings{2017_ACCVW_Yu,
    author = "Yu, Jongmin and Park, Sangwoo and Lee, Sangwook and Jeon, Moongu",
    booktitle = "ACCV",
    title = "Representation learning, scene understanding, and feature fusion for drowsiness detection",
    year = "2016"
}

Shih et al., MSTN: Multistage Spatial-Temporal Network for Driver Drowsiness Detection, ACCVW, 2017 | paper

Dataset(s): DDD

@inproceedings{2017_ACCVW_Shih,
    author = "Shih, Tun-Huai and Hsu, Chiou-Ting",
    booktitle = "ACCV",
    title = "MSTN: Multistage spatial-temporal network for driver drowsiness detection",
    year = "2016"
}

Huynh et al., Detection of Driver Drowsiness Using 3D Deep Neural Network and Semi-Supervised Gradient Boosting Machine, ACCVW, 2017 | paper

Dataset(s): DDD

@inproceedings{2017_ACCVW_Huynh,
    author = "Huynh, Xuan-Phung and Park, Sang-Min and Kim, Yong-Guk",
    booktitle = "ACCV",
    title = "Detection of driver drowsiness using 3D deep neural network and semi-supervised gradient boosting machine",
    year = "2016"
}

Weng et al., Driver Drowsiness Detection via a Hierarchical Temporal Deep Belief Network, ACCV, 2017 | paper

Dataset(s): DDD

@inproceedings{2017_ACCV_Weng,
    author = "Weng, Ching-Hua and Lai, Ying-Hsiu and Lai, Shang-Hong",
    booktitle = "ACCV",
    title = "Driver drowsiness detection via a hierarchical temporal deep belief network",
    year = "2016"
}

Yin et al., A Driver Fatigue Detection Method Based on Multi-Sensor Signals, WACV, 2016 | paper

Dataset(s): private

@inproceedings{2016_WACV_Yin,
    author = "Yin, Hao and Su, Yuanqi and Liu, Yuehu and Zhao, Danchen",
    booktitle = "WACV",
    title = "A driver fatigue detection method based on multi-sensor signals",
    year = "2016"
}

Kim et al., Fusion of Driver-information Based Driver Status Recognition for Co-Pilot System, IV, 2016 | paper

Dataset(s): private

@inproceedings{2016_IV_Kim,
    author = "Kim, Jinwoo and Kim, Kitae and Yoon, Daesub and Koo, Yongbon and Han, Wooyong",
    booktitle = "2016 Ieee Intelligent Vehicles Symposium (iv)",
    organization = "IEEE",
    pages = "1398--1403",
    title = "Fusion of driver-information based driver status recognition for co-pilot system",
    year = "2016"
}

Choi et al., Tracking a Driver’s Face against Extreme Head Poses and Inference of Drowsiness Using a Hidden Markov Model, Applied Sciences, 2016 | paper

Dataset(s): private

@article{2016_ApplSci_Choi,
    author = "Choi, In-Ho and Jeong, Chan-Hee and Kim, Yong-Guk",
    journal = "Applied Sciences",
    number = "5",
    pages = "137",
    title = "Tracking a driver’s face against extreme head poses and inference of drowsiness using a hidden Markov model",
    volume = "6",
    year = "2016"
}

Park et al., Driver drowsiness detection system based on feature representation learning using various deep networks, ACCV, 2016 | paper

Dataset(s): DDD

@inproceedings{2016_ACCV_Park,
    author = "Park, Sanghyuk and Pan, Fei and Kang, Sunghun and Yoo, Chang D",
    booktitle = "ACCV",
    title = "Driver drowsiness detection system based on feature representation learning using various deep networks",
    year = "2016"
}

Wang et al., Driver drowsiness detection based on non-intrusive metrics considering individual specifics, Accident Analysis and Prevention, 2016 | paper

Dataset(s): private

@article{2016_AccidentAnalysis_Wang,
    author = "Wang, Xuesong and Xu, Chuan",
    journal = "Accident Analysis \\& Prevention",
    pages = "350--357",
    publisher = "Elsevier",
    title = "Driver drowsiness detection based on non-intrusive metrics considering individual specifics",
    volume = "95",
    year = "2016"
}

Chang et al., Driver Fatigue Surveillance via Eye Detection, ITSC, 2014 | paper

Dataset(s): private

@inproceedings{2014_ITSC_Chang,
    author = "Chang, Tang-Hsien and Chen, Yi-Ru",
    booktitle = "ITSC",
    title = "Driver fatigue surveillance via eye detection",
    year = "2014"
}

Tadesse et al., Driver Drowsiness Detection through HMM based Dynamic Modeling, ICRA, 2014 | paper

Dataset(s): private

@inproceedings{2014_ICRA_Tadesse,
    author = "Tadesse, Eyosiyas and Sheng, Weihua and Liu, Meiqin",
    booktitle = "ICRA",
    title = "Driver drowsiness detection through HMM based dynamic modeling",
    year = "2014"
}

Mbouna et al., Visual Analysis of Eye State and Head Pose for Driver Alertness Monitoring, Trans. ITS, 2013 | paper

Dataset(s): BU HeadTracking, private

@article{2013_T-ITS_Mbouna,
    author = "Mbouna, Ralph Oyini and Kong, Seong G and Chun, Myung-Geun",
    journal = "IEEE Transactions on Intelligent Transportation Systems",
    number = "3",
    pages = "1462--1469",
    title = "Visual analysis of eye state and head pose for driver alertness monitoring",
    volume = "14",
    year = "2013"
}

Masala et al., Detecting Driver Inattention by Rough Iconic Classification, IV, 2013 | paper

Dataset(s): private

@inproceedings{2013_IV_Masala,
    author = "Masala, Giovanni Luca and Grosso, Enrico",
    booktitle = "2013 IEEE Intelligent Vehicles Symposium (IV)",
    organization = "IEEE",
    pages = "913--918",
    title = "Detecting driver inattention by rough iconic classification",
    year = "2013"
}

Li et al., Vision-based Estimation of Driver Drowsiness with ORD Model Using Evidence Theory, IV, 2013 | paper

Dataset(s): private

@inproceedings{2013_IV_Li,
    author = "Li, Xuanpeng and Seignez, Emmanuel and Loonis, Pierre",
    booktitle = "IV",
    title = "Vision-based estimation of driver drowsiness with ORD model using evidence theory",
    year = "2013"
}

Akrout et al., A visual based approach for drowsiness detection, IV, 2013 | paper

Dataset(s): private

@inproceedings{2013_IV_Akrout,
    author = "Akrout, Belhassen and Mahdi, Walid",
    booktitle = "IV",
    title = "A visual based approach for drowsiness detection",
    year = "2013"
}

Jin et al., Driver Sleepiness Detection System Based onEye Movements Variables, Advances in Mechanical Engineering, 2013 | paper

Dataset(s): private

@article{2013_AdvMechEng_Jin,
    author = "Jin, Lisheng and Niu, Qingning and Jiang, Yuying and Xian, Huacai and Qin, Yanguang and Xu, Meijiao",
    journal = "Advances in Mechanical Engineering",
    pages = "648431",
    title = "Driver sleepiness detection system based on eye movements variables",
    volume = "5",
    year = "2013"
}

Garcia et al., Vision-based drowsiness detector for Real Driving Conditions, IV, 2012 | paper

Dataset(s): private

@inproceedings{2012_IV_Garcia,
    author = "Garcia, I and Bronte, Sebastian and Bergasa, Luis Miguel and Almaz{\'a}n, Javier and Yebes, J",
    booktitle = "IV",
    title = "Vision-based drowsiness detector for real driving conditions",
    year = "2012"
}

Matsuo et al., Prediction of Drowsy Driving by Monitoring Driver’s Behavior, ICPR, 2012 | paper

Dataset(s): private

@inproceedings{2012_ICPR_Matsuo,
    author = "Matsuo, Haruo and Khiat, Abdelaziz",
    booktitle = "ICPR",
    title = "Prediction of drowsy driving by monitoring driver's behavior",
    year = "2012"
}

Jo et al., Vision-based method for detecting driver drowsiness and distraction in driver monitoring system, Optical Engineering, 2011 | paper

Dataset(s): private

@article{2011_OptEng_Jo,
    author = "Jo, Jaeik and Lee, Sung Joo and Kim, Jaihie and Jung, Ho Gi and Park, Kang Ryoung",
    journal = "Optical Engineering",
    number = "12",
    pages = "127202",
    title = "Vision-based method for detecting driver drowsiness and distraction in driver monitoring system",
    volume = "50",
    year = "2011"
}

Flores et al., Driver drowsiness detection system under infrared illumination for an intelligent vehicle, IET Intelligent Transport Systems, 2011 | paper

Dataset(s): private

@article{2011_IET_Flores,
    author = "Flores, Marco Javier and Armingol, J Ma and de la Escalera, Arturo",
    journal = "IET Intelligent Transport Systems",
    number = "4",
    pages = "241--251",
    publisher = "IET",
    title = "Driver drowsiness detection system under infrared illumination for an intelligent vehicle",
    volume = "5",
    year = "2011"
}

Fan et al., Gabor-based dynamic representation for human fatigue monitoring in facial image sequences, Pattern Recognition Letter, 2010 | paper

Dataset(s): private

@article{2010_PRL_Fan,
    author = "Fan, Xiao and Sun, Yanfeng and Yin, Baocai and Guo, Xiuming",
    journal = "Pattern Recognition Letters",
    number = "3",
    pages = "234--243",
    title = "Gabor-based dynamic representation for human fatigue monitoring in facial image sequences",
    volume = "31",
    year = "2010"
}

Zhang et al., A new real-time eye tracking based on nonlinear unscented Kalman filter for monitoring driver fatigue, Journal of Control Theory Applications, 2010 | paper

Dataset(s): private

@article{2010_JCTA_Zhang,
    author = "Zhang, Zutao and Zhang, Jiashu",
    journal = "Journal of Control Theory and Applications",
    number = "2",
    pages = "181--188",
    publisher = "Springer",
    title = "A new real-time eye tracking based on nonlinear unscented Kalman filter for monitoring driver fatigue",
    volume = "8",
    year = "2010"
}

Friedrichs et al., Camera-based Drowsiness Reference for Driver State Classification under Real Driving Conditions, IV, 2010 | paper

Dataset(s): private

@inproceedings{2010_IV_Friedrichs,
    author = "Friedrichs, Fabian and Yang, Bin",
    booktitle = "IV",
    title = "Camera-based drowsiness reference for driver state classification under real driving conditions",
    year = "2010"
}