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Fatigue driving is bringing more and more serious harm, but there are various reasons for fatigue driving, it is still difficult to test the driver’s fatigue. This paper defines a method to test driver’s fatigue based on the EEG, and different from other researches into fatigue driving, this paper mainly takes the fatigue features of EEG signals in fatigue state and uses wavelet entropy as the feature extraction method to analyze the features of wavelet entropy and spectral entropy features as well as the classification accuracy under the same classifier. The SVM is used to show the classifier’s results. The accuracy of the driver fatigue state monitoring using the wavelet entropy is 90.7%, which is higher than the use of spectral entropy as the characteristic accuracy rate of 81.3%. The results show that the frequency characteristics of EEG can be well applied to driving fatigue testing, but different frequency feature calculation methods will affect the classification accuracy.

In order to reduce the serious problems caused by the operators’ fatigue, we propose a novel network model Convolutional Neural Network and Long Short-Term Memory Network (CNN-LSTM) — for fatigue detection in the inter-frame images of video sequences, which mainly consists of CNN and LSTM network. Firstly, in order to improve the accuracy of the deep network structure, the Viola–Jones detection algorithm and the Kernelized Correlation Filter (KCF) tracking algorithm are used in the face detection to normalize the size of the inter-frame images of video sequences. Secondly, we use the CNN and the LSTM network to detect the fatigue state in real time and efficiently. The fatigue-related facial features are extracted by the CNN. Then, the temporal symptoms of the whole fatigue process can be extracted by LSTM networks, the input data which is the facial feature vector can be obtained by the CNN. Thirdly, we train and test the network in a step-by-step approach. Finally, we experiment with the proposed network model. The experimental results demonstrate that the network structure can effectively detect the fatigue state, and the overall accuracy rate can rise to 82.8%.

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.

Driver misbehavior is one of the major traffic safety hazards as car ownership increases year by year. So, it is important to have driver fatigue detection and behavior recognition. Initially, given the fatigue detection problem that the images captured by the visible light camera cannot capture the eyes of a driver wearing sunglasses or eye glasses. As a solution, this paper introduces a DCT-HSV preprocessing algorithm for infrared images, which is believed to enhance the target characteristics of infrared images. The paper also introduces a more efficient lightweight SSD detection model, which achieves a better balance in terms of model size and detection performance. It also shows a better performance in self-built datasets and basic vehicle operation datasets. Secondly, aiming at the problems of high complexity and poor accuracy of the existing driving behavior detection model, this paper designs a driver abnormal behavior discrimination model based on the comparison twin, which has good performance on the Kaggle public dataset. The relevant experimental detection results show that the method constructed in this paper has high detection accuracy, low warning delay, and good practical use value.

In transportation, drivers’ state directly affects traffic safety. Therefore, an accurate driver’s fatigue detection is crucial for ensuring driving safety. Real-time and accurate technology is needed for driver fatigue detection. To address this problem, this article proposes a fatigue detection method based on an improved deep learning and personalized framework. First, clustering is applied to face size, and cluster numbers are used to determine the number of detection layers. Then, the size of anchor boxes is set according to the face size. In the proposed framework, the number of convolutional networks is set according to the principle that the receptive field should match the face size in the predicted feature map. Finally, a variety of fatigue features are learned by minimizing the loss function. In addition, a personalized face fatigue detection method is put forward for building a fatigue detection framework to judge the driver’s fatigue status more reasonably. The experimental results show that the proposed method based on an improved clustering method and local receptive field can improve the detection speed of driver’s fatigue while maintaining high detection accuracy. The proposed method can reach 125 fps by using GPU GeForce GTX TITAN, which satisfies the real-time requirement. In addition, the personalized framework can achieve high detection accuracy while keeping acceptable speed. The proposed model can accurately and timely detect driver fatigue, which can help to avoid accidents.

Fatigue detection of workers is an important factor in construction site monitoring. Nowadays, worker exhaustion on construction sites causes tiredness and drowsiness. The prediction of mental exhaustion is critical because the job has increased over the years. Accurate fatigue detection is important for analyzing the stress level of work on construction sites. However, recording worker activities and detecting fatigue is critical for site supervisors. Over the last century, there has been an increasing trend towards vision-based action recognition. The identification of worker activities in far-field surveillance video has received good attention. Henceforth, this research proposed a hierarchical statistical approach for detecting worker activity in far-field security footage. In this paper, extension based equilibrium with capsule autoencoder network is proposed for fatigue detection. To improve the prediction performance, video data is collected for monitoring. Initially, the video dataset is converted into frames, and these frames are pre-processed using normalization. Afterwards, statistical feature extraction techniques are extracted from the normalized frames to improve the prediction performance. It helps to identify the symptoms of fatigue detection. The persons with the symptoms are identified from the extraction. Feature selection is performed using hybrid Battle Royale Optimization and Particle Swarm Optimization algorithms (hybrid BRO-PSO). Finally, based on the selected features, fatigue classification is performed using extended equilibrium optimization with Capsule Autoencoder (EECAEN). The performance of the proposed scheme is compared with different existing approaches. The performance of fatigue detection is analyzed with real time video dataset. The performance of the proposed approach is compared with different existing techniques such as Convolution Neural Network (CNN), Long Short-Term Memory (LSTM), and Recurrent Neural Network (RNN). The proposed method outperforms the existing approaches in terms of F1-score, accuracy, recall, precision, specificity, and sensitivity.