scholarly journals Driver Fatigue Detection Based on Residual Channel Attention Network and Head Pose Estimation

2021 ◽  
Vol 11 (19) ◽  
pp. 9195
Author(s):  
Mu Ye ◽  
Weiwei Zhang ◽  
Pengcheng Cao ◽  
Kangan Liu
Keyword(s):  
Pose Estimation ◽  
Classification Accuracy ◽  
Detection System ◽  
Mouth Opening ◽  
The State ◽  
Head Pose Estimation ◽  
Head Pose ◽  
Driver Fatigue ◽  
Attention Network ◽  
Fatigue Detection

Driver fatigue is the culprit of most traffic accidents. Visual technology can intuitively judge whether the driver is in the state of fatigue. A driver fatigue detection system based on the residual channel attention network (RCAN) and head pose estimation is proposed. In the proposed system, Retinaface is employed for face location and outputs five face landmarks. Then the RCAN is proposed to classify the state of eyes and the mouth. The RCAN includes a channel attention module, which can adaptively extract key feature vectors from the feature map, which significantly improves the classification accuracy of the RCAN. In the self-built dataset, the classification accuracy of the eye state of the RCAN reaches 98.962% and that of the mouth state reaches 98.561%, exceeding other classical convolutional neural networks. The percentage of eyelid closure over the pupil over time (PERCLOS) and the mouth opening degree (POM) are used for fatigue detection based on the state of eyes and the mouth. In addition, this article proposes to use a Perspective-n-Point (PnP) method to estimate the head pose as an essential supplement for driving fatigue detection and proposes over-angle to evaluate whether the head pose is excessively deflected. On the whole, the proposed driver fatigue system integrates 3D head pose estimation and fatigue detection based on deep learning. This system is evaluated by the four datasets and shows success of the proposed method with their high performance.

Driver Fatigue Detection Based On Facial Feature Analysis

2021 ◽  
pp. 2150034
Author(s):  
Yimin Zhang ◽  
Xianwei Han ◽  
Wei Gao ◽  
Yunliang Hu
Keyword(s):  
Traffic Accidents ◽  
Detection System ◽  
Recognition Rate ◽  
Ccd Camera ◽  
Facial Feature ◽  
The State ◽  
Driver Fatigue ◽  
Eyelid Closure ◽  
Fatigue Driving ◽  
Fatigue Detection

Fatigue driving is one of the main causes of traffic accidents. In recent years, considerable attention has been paid to fatigue detection systems, which is an important solution for preventing fatigue driving. In order to prevent and reduce fatigue driving, a driver fatigue detection system based on computer vision is proposed. In this system, an improved face detection method is used to detect the driver’s face from the image obtained by a charge coupled device (CCD) camera. Then, the feature points of the eyes and mouth are located by an ensemble of regression trees. Next, fatigue characteristic parameters are calculated by the improved percentage of eyelid closure over the pupil over time algorithm. Finally, the state of drivers is evaluated by using a fuzzy neural network. The system can effectively monitor and remind the state of drivers so as to significantly avoid or decrease the occurrence of traffic accidents. The experimental results show that the system is of wonderful real-time performance and accurate recognition rate, so it meets the requirements of practicality in driver fatigue detection greatly.

scholarly journals New Features for Webcam Proctoring Using Python and Opencv

2021 ◽  
Vol 11 (2) ◽  
pp. 1497-1513
Author(s):  
Harish S.
Keyword(s):  
Mobile Phone ◽  
Pose Estimation ◽  
Mouth Opening ◽  
Head Pose Estimation ◽  
Head Pose ◽  
Learning Models ◽  
Spoofing Detection ◽  
Log File ◽  
Face Spoofing ◽  
Machine Learning Models

Online examinations have turned out to be the new normal. However, it is not that easy to proctor the students as rigorously as in in-center examinations. It is essential to find an approach to proctor the online examinations too as rigorously as possible. There are already several webcam proctoring systems that are used in the real world, but these systems are not very accurate and miss out on detecting all possible malpractices and in certain cases due to defect in the system it detects a malpractice for someone who never even attempted any. This project focuses mainly on building features that can make the existing webcam proctoring system more advanced and rigorous. The project is aimed at building the following features namely head pose estimation, mouth opening detection, eye ball monitoring, number of persons detection, mobile phone detection and face spoofing detection. For each of these features, machine learning models are built using Python. All these features make use of the live webcam feed which is obtained using OpenCV and an output is obtained which gives information about the direction of the head and eyes, presence of more than one person and presence of mobile phone, opening of mouth, occurrence of face spoofing. All these outputs are recorded as a log file which can be used to identify any possible malpractices based on these features.

The More, the Merrier? A Study on In-Car IR-based Head Pose Estimation

2020 ◽  
Author(s):  
Ahmet Firintepe ◽  
Mohamed Selim ◽  
Alain Pagani ◽  
Didier Stricker
Keyword(s):  
Pose Estimation ◽  
Head Pose Estimation ◽  
Head Pose

Early Warning Pedestrian Crossing Intention From Its Head Gesture using Head Pose Estimation

2021 ◽  
Author(s):  
Muhammad Ilham Perdana ◽  
Wiwik Anggraeni ◽  
Hanugra Aulia Sidharta ◽  
Eko Mulyanto Yuniarno ◽  
Mauridhi Hery Purnomo
Keyword(s):  
Pose Estimation ◽  
Early Warning ◽  
Head Pose Estimation ◽  
Head Pose ◽  
Pedestrian Crossing
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