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Driver Fatigue Detection Based on Facial Key Points and LSTM
2021
Security and Communication Networks
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In order to detect whether the driver has fatigue driving, this paper proposes a fatigue state recognition algorithm. ...
The method first uses MTCNN (multitask convolutional neural network) to detect human face, and then DLIB (an open-source software library) is used to locate facial key points to extract the fatigue feature ...
detection algorithm based on facial key points and long short-term memory. ...
doi:10.1155/2021/5383573
fatcat:7hoyycbatnev3drzn2q26flfcy
Fatigue Driving Recognition Method Based on Multi-Scale Facial Landmark Detector
2022
Electronics
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points on the face. ...
To maximize the driver's facial feature information and temporal characteristics, a fatigue driving behavior recognition method based on a multi-scale facial landmark detector (MSFLD) is proposed. ...
The MSFLD method based on deep learning are proposed to adaptively detect facial key points. ...
doi:10.3390/electronics11244103
fatcat:r3kwkz63t5b7hlerzyk7nalyvy
Fatigue Driving Detection Based on Deep Learning and Multi-Index Fusion
2021
IEEE Access
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This work was supported in part by the National Natural Science Foundation of China (research on the theory and method of visual gesture remote operation control of ground reconnaissance robot in all-day ...
To solve these problems, this paper studies a driving fatigue detection algorithm based on deep learning and facial multi-index fusion, which mainly includes face location and facial key points detection ...
) to locate the face and detect the facial key points. ...
doi:10.1109/access.2021.3123388
fatcat:65sylcr7lfa5zcrkgfedoll6vq
Driver Fatigue Detection Method Based on Human Pose Information Entropy
2022
Journal of Advanced Transportation
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Driver fatigue detection (DFD) is an effective method to prevent traffic accidents. The existing research on DFD using facial features is an effective and noninvasive fatigue detection method. ...
These information entropy values are combined resorting to the support vector machine (SVM) to recognize the driver fatigue state. ...
key points are located based on the self-built data set before fatigue feature extraction, and the result is shown in Figure 9 : When the driver is driving in a normal state, he/she can observe the road ...
doi:10.1155/2022/7213841
fatcat:v4bcgx6qjnal7oeavye4422yqm
Research on a Real-Time Driver Fatigue Detection Algorithm Based on Facial Video Sequences
2022
Applied Sciences
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This paper proposes a real-time comprehensive driver fatigue detection algorithm based on facial landmarks to improve the detection accuracy, which detects the driver's fatigue status by using facial video ...
A tasks-constrained deep convolutional network is constructed to detect the face region based on 68 key points, which can solve the optimization problem caused by the different convergence speeds of each ...
As far as we know, the current driving fatigue detection generally makes a single detection based on the state of the eyes or mouth, and there is little research on the comprehensive detection based on ...
doi:10.3390/app12042224
fatcat:bg4urbcn6nfl5h65ij6qc4artm
A Fatigue Driving Detection Algorithm Based on Facial Motion Information Entropy
2020
Journal of Advanced Transportation
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Research studies on machine vision-based driver fatigue detection algorithm have improved traffic safety significantly. ...
We use FFV as an indicator to determine whether the driver is in fatigue state. Finally, we design a sliding window to get the facial information entropy. ...
Face detection and facial feature point location are the basis of fatigue driving detection. e FFV of each frame in the on-board video is calculated and stored based on the facial feature points. ...
doi:10.1155/2020/8851485
fatcat:cnixlst6irh7nhdwscmhfhkixa
Driver Emotion and Fatigue State Detection Based on Time Series Fusion
2022
Electronics
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In this paper, we propose a non-invasive and efficient detection method for driver fatigue and emotional state, which is the first time to combine them in the detection of driver state. ...
Firstly, the captured video image sequences are preprocessed, and Dlib (image open source processing library) is used to locate face regions and mark key points; secondly, facial features are extracted ...
For driver fatigue detection based on visual information, Zhu et al. designed a driver fatigue detection algorithm based on facial key points by constructing a deep convolutional network to detect face ...
doi:10.3390/electronics12010026
fatcat:gng7g6ggjzh35fxjddhvxypvae
Real-Time Driver Drowsiness Detection System using Facial Features
2020
International Journal for Research in Applied Science and Engineering Technology
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Further, we have a design replacement detection technique for facial regions supporting sixty eight key points. Then we have a tendency to use these facial regions to gauge the drivers' state. ...
Once a driver is in a state of fatigue, the facial expressions, e.g The frequency of blinking and yawning, are completely different from those within the traditional state. ...
Then, we use Dlib to locate 68 facial key points on the driver's face. ...
doi:10.22214/ijraset.2020.30392
fatcat:3ccvnptfzrdxnds3leyaimc73m
Fatigue Driving Detection Method Based on Combination of BP Neural Network and Time Cumulative Effect
2022
Sensors
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Then, a cascade regression tree-based method was used to detect the 68 facial landmarks and an improved method combining key points and image processing was adopted to calculate the eye aspect ratio (EAR ...
Finally, by introducing the Sigmoid function, a fatigue detection model considering the time accumulation effect was established, and the drivers' fatigue state was identified segment by segment through ...
No Fatigue state was judged based points are detected on the time accumulation successfully? ...
doi:10.3390/s22134717
pmid:35808213
pmcid:PMC9269348
fatcat:56vcseiljfgpfc7i2egiit6ij4
Real-Time Driver-Drowsiness Detection System Using Facial Features
2019
IEEE Access
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Further, we designed a new detection method for facial regions based on 68 key points. Then we use these facial regions to evaluate the drivers' state. ...
When a driver is in a state of fatigue, the facial expressions, e.g., the frequency of blinking and yawning, are different from those in the normal state. ...
We define the facial regions of detection based on facial key points. Moreover, we introduce a new evaluation method for drowsiness based on the states of the eyes and mouth. ...
doi:10.1109/access.2019.2936663
fatcat:jqdhhp2orjcgfexu6qqgcnczh4
Driver Fatigue Level Prediction
2022
International journal of computer science and mobile computing
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Further, we designed a new detection method for facial regions based on 68 key points. Then we use these facial regions to evaluate the drivers' state. ...
When a driver is in a state of fatigue, the facial expressions, e.g., the frequency of blinking, are different from those in the normal state. ...
We define the facial regions of detection based on facial key points. Moreover, we introduce a new evaluation method for drowsiness based on the states of the eyes and mouth. ...
doi:10.47760/ijcsmc.2022.v11i11.016
fatcat:33nqpjoj75a4lpbtcdeh2qnkhu
A Survey on Mobile Sensing Based Mood-Fatigue Detection for Drivers
[chapter]
2016
Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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With the help of mobile sensing and mood-fatigue detection, drivers' moodfatigue status can be detected while driving, and then appropriate measures can be taken to eliminate the fatigue or negative mood ...
Finally, we summarize the major challenges in mood-fatigue detection of drivers, and outline the future research directions. ...
Local feature based algorithms [26] exploits curvelet change in two routes i.e. as a key point finder to extract salient points on the face region. ...
doi:10.1007/978-3-319-33681-7_1
fatcat:kjycg56eybhypn7a7pe2uy53du
A fatigue driving detection algorithm based on facial multi-feature fusion
2020
IEEE Access
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The paper proposes a fatigue driving detection algorithm based on facial multifeature fusion combining driver characteristics. ...
Researches on machine vision-based driver fatigue detection algorithm have improved traffic safety significantly. Generally, many algorithms do not analyze driving state from driver characteristics. ...
In the face key point detection, Dlib adopts the method in [46] - [48] and provides a model trained based on millions of faces. ...
doi:10.1109/access.2020.2998363
fatcat:fogvsg7mtrgxpg6r27enjo7lqu
Advanced Driver Assistance System for Drivers using Machine Learning and Artificial Intelligence Techniques
2022
International Journal for Research in Applied Science and Engineering Technology
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When a driver is in a state of fatigue, the facial expressions, e.g., the frequency of blinking and yawning, are different from those in the normal state. ...
In this paper, we propose a system called "Advanced Driver Assistant System", which detects the drivers fatigue status, such as yawning, blinking, and duration of eye closure, using video images, without ...
To meet the real-time performance of the system, we use the MC-KCF algorithm to track the driver's face and recognize the facial key regions based on key-point detection. ...
doi:10.22214/ijraset.2022.43260
fatcat:pe4csasevfdepjxujcukxcudsa
Convolutional Two-Stream Network Using Multi-Facial Feature Fusion for Driver Fatigue Detection
2019
Future Internet
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In this paper, we present a driver fatigue detection algorithm using two-stream network models with multi-facial features. ...
The main contribution of this paper is the combination of a two-stream network and multi-facial features for driver fatigue detection. ...
Conclusions We proposed a driver fatigue detection algorithm based on multi-facial feature fusion, which not only avoided peripheral equipment on the driver's body, but also had high accuracy. ...
doi:10.3390/fi11050115
fatcat:2xxvldbuo5bl5fzagrnkbrmkju
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