Truck Driver Fatigue Detection Based on Video Sequences in Open-Pit Mines

Due to complex background interference and weak space–time connection, traditional driver fatigue detection methods perform poorly for open-pit truck drivers. For these issues, this paper presents a driver fatigue detection method based on Libfacedetection and an LRCN. The method consists of three stages: (1) using a face detection module with a tracking method to quickly extract the ROI of the face; (2) extracting and coding the features; (3) combining the coding model to build a spatiotemporal classification network. The innovation of the method is to utilize the spatiotemporal features of the image sequence to build a spatiotemporal classification model suitable for this task. Meanwhile, a tracking method is added to the face detection stage to reduce time expenditure. As a result, the average speed with the tracking method for face detection on video is increased by 74% in comparison with the one without the tracking method. Our best model adopts a DHLSTM and feature-level frame aggregation, which achieves high accuracy of 99.30% on the self-built dataset.

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

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.

Method of biocontrol of vehicle driver fatigue

This article describes an example of negligence of drivers transporting passengers and methods of solving it using modern inventions. One of these troubles is driving a car and moving passengers by taxi driver in a tired state. Since not every driver can correctly assess their psycho-physical condition, so to do this, scientists began to create devices for tracking human behavior when he drives vehicle. The purpose of implementing driver fatigue monitoring systems is to ensure road safety and preserve lives and property of citizens. The use of these systems is to facilitate the work of emergency services and taxi company owners, taxi drivers and their passengers. In our article we want to touch on the problem of overwork, specifically taxi drivers, since their work activity is socially significant and non-compliance with the norms of work and rest periods can lead to tragic consequences. Modern taxi drivers often rely on a strong body of car and electronic gadgets in an unexpected situation on the road. Therefore, when driving a car, despite being overworked, they allow themselves to relax beyond the limit and dont react in time if an emergency occurs. We have studied options for implementing driver fatigue monitoring systems and offer to install them on a taxi car.