Driver Fatigue Detection Method Based on Eye States With Pupil and Iris Segmentation

Zhuang, Qianyang; Zhang, Kehua; Wang, Jiayi; Chen, Qianqian

doi:10.1109/access.2020.3025818

Cited by 49 publications

(16 citation statements)

References 31 publications

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“…According to the o cial website of the National Bureau of Statistics, by the end of 2019, the number of civil cars in China reached 253.7638 million, with a year-on-year growth of 9.23%. e number of car drivers reached 39,7528,600, with a year-on-year increase of 7.66% [1]. Cars greatly facilitate people's travel.…”

Section: Introductionmentioning

confidence: 99%

Driving Detection Based on the Multifeature Fusion

Yang¹,

Lu²

2022

Journal of Control Science and Engineering

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In order to solve the problems of facial feature localization and driver fatigue state identification methods in driving fatigue detection, a driving detection method based on the multifeature fusion was proposed. This method uses a supervised descent algorithm to simultaneously locate multiple facial features of drivers. On the basis of blink, yawn and nod judgment, multiple characteristic values of blink frequency, yawn frequency, and nod frequency of drivers were extracted to establish a fatigue detection sample database, and a naive Bayes classifier was constructed to judge fatigue. When the driver appears fatigue driving, warning information is given in time in order to prevent traffic accidents. The experimental results show that two sample videos were selected for testing. The accuracy rate of video sample 1 and video sample 2 was 94.74% and 95.00%, respectively. Conclusion. In the actual driving environment video test results, the discriminant average accuracy of a driver fatigue state reaches 94.87%, which has a good performance.

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Section: Introductionmentioning

confidence: 99%

Driving Detection Based on the Multifeature Fusion

Yang¹,

Lu²

2022

Journal of Control Science and Engineering

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“…The proposed PFTL-DDD method is evaluated on the NTHU-DDD and YAWDD benchmark video datasets which are widely used in driver drowsiness detection researches [34][35][36][37][38][39][40][41][42][43]. The NTHU-DDD is an open-source driver drowsiness video dataset collected by the Computer Vision Lab of National Tsing Hua University [7].…”

Section: A Datasetmentioning

confidence: 99%

Privacy-Preserving Federated Transfer Learning for Driver Drowsiness Detection

et al. 2022

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The drowsiness affects the driver's sensory, cognitive, and psychomotor abilities, which are necessary for safe driving. Drowsiness detection is a critical technique to avoid traffic accidents. The federated learning (FL) can solve the problem of insufficient driver facial data by utilizing different industrial entities' data. However, in the FL system, the privacy information of the drivers might be leaked. In addition, reducing the communication costs and maintaining the model performance is also a challenge in industrial scenarios. In this work, we propose a federated transfer learning method with privacypreserving protocol for driver drowsiness detection, named PFTL-DDD. We use fine-tuning transfer learning on the initial model of the drowsiness detection FL system. Furthermore, a CKKS-based privacypreserving protocol is applied to preserve the drivers' privacy data by encrypting the exchanged parameters. The experimental results show that the PFTL-DDD method is superior in terms of accuracy and efficiency as compared to the conventional federated learning on the NTHU-DDD dataset and YAWDD dataset. The theoretical analysis demonstrates that the proposed transfer learning method can reduce the communication cost of the system, and the CKKS-based security protocol can protect the personal privacy.INDEX TERMS Driver drowsiness detection, transfer learning, federated learning, privacy-preserving.

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“…This algorithm uses 4 main factors namely the Haar feature, Integral Image, Cascade Classifier, and Adaboost machine-learning. Also, the frontal face detector provided by DLIB is used in [74] by extracting features from the histogram of oriented gradients (HOG), which are then passed through an SVM is used to estimate the location of 68 coordinates (x, y) that map the facial points on a person's face. It can detect and describe important facial features such as: eyes, eyebrows, nose, mouth and jawline).…”

Section: 5mentioning

confidence: 99%

A Systematic Literature Review of Driver Inattention Monitoring Systems for Smart Car

Soultana¹,

Benabbou

Sael³

et al. 2022

Int. J. Interact. Mob. Technol.

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In recent years, a significant increase in road accidents worldwide has been observed. This can partly be due to either driver distraction or fatigue. Therefore, a reliable alerting system that can detect the driver's inattention including fatigue, sleep, and distraction is necessarily required to prevent any potential accidents. The aim of this paper is to conduct a systematic review of literature (SLR) on monitoring driver inattention. In particular, the present study deals with different aspects of prior studies such as the sensors used; the types of data, the feature engineering techniques, the machine-learning techniques applied and their performance along with, the dataset used, etc. anotherFour approaches can be depicted from literature according to indicators they are based on: physiological, physical, driver performance and hybrid approach. We will focus on these different approaches in order to answer different questions, starting with the type of indicators used in the case of distraction or fatigue detection, the different datasets employed, the feature extraction techniques and the machine learning models applied. Furthermore, the study examines the practicality and reliability of each of the four approaches, as well as possible future prospects in the area, and highlights new challenges in the field of driver inattention detection with both forms of fatigue and distraction.

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Driver Fatigue Detection Method Based on Eye States With Pupil and Iris Segmentation

Cited by 49 publications

References 31 publications

Driving Detection Based on the Multifeature Fusion

Driving Detection Based on the Multifeature Fusion

Privacy-Preserving Federated Transfer Learning for Driver Drowsiness Detection

A Systematic Literature Review of Driver Inattention Monitoring Systems for Smart Car

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