Real-time classification for autonomous drowsiness detection using eye aspect ratio

Maior, Caio Bezerra Souto; Moura, Márcio José das Chagas; Lins, Isis Didier

doi:10.1016/j.eswa.2020.113505

Cited by 124 publications

(49 citation statements)

References 75 publications

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“…The field of computational learning includes machine learning (ML) and deep learning (DL), aiming to detect meaningful patterns in data automatically and to solve problems, which are impossible (or impractical) to be represented by explicit algorithms [ 19 ]. Traditional ML techniques have already been successfully applied to a diversity of pattern recognition and regression tasks [ 20 – 22 ]. DL learns high-level abstractions in data by utilizing hierarchical architectures [ 23 ].…”

Section: Overview Of Cnn and Covid-19 Diagnostic Modelsmentioning

confidence: 99%

Convolutional neural network model based on radiological images to support COVID-19 diagnosis: Evaluating database biases

et al. 2021

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As SARS-CoV-2 has spread quickly throughout the world, the scientific community has spent major efforts on better understanding the characteristics of the virus and possible means to prevent, diagnose, and treat COVID-19. A valid approach presented in the literature is to develop an image-based method to support COVID-19 diagnosis using convolutional neural networks (CNN). Because the availability of radiological data is rather limited due to the novelty of COVID-19, several methodologies consider reduced datasets, which may be inadequate, biasing the model. Here, we performed an analysis combining six different databases using chest X-ray images from open datasets to distinguish images of infected patients while differentiating COVID-19 and pneumonia from ‘no-findings’ images. In addition, the performance of models created from fewer databases, which may imperceptibly overestimate their results, is discussed. Two CNN-based architectures were created to process images of different sizes (512 × 512, 768 × 768, 1024 × 1024, and 1536 × 1536). Our best model achieved a balanced accuracy (BA) of 87.7% in predicting one of the three classes (‘no-findings’, ‘COVID-19’, and ‘pneumonia’) and a specific balanced precision of 97.0% for ‘COVID-19’ class. We also provided binary classification with a precision of 91.0% for detection of sick patients (i.e., with COVID-19 or pneumonia) and 98.4% for COVID-19 detection (i.e., differentiating from ‘no-findings’ or ‘pneumonia’). Indeed, despite we achieved an unrealistic 97.2% BA performance for one specific case, the proposed methodology of using multiple databases achieved better and less inflated results than from models with specific image datasets for training. Thus, this framework is promising for a low-cost, fast, and noninvasive means to support the diagnosis of COVID-19.

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Section: Overview Of Cnn and Covid-19 Diagnostic Modelsmentioning

confidence: 99%

Convolutional neural network model based on radiological images to support COVID-19 diagnosis: Evaluating database biases

et al. 2021

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“…In a real-time environment, it is important to detect and monitor driver behavior to save human lives. To resolve this problem, there were many automatic driver fatigue detection systems [ 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 ] developed in past studies. Several computer vision-based applications were developed in the past to detect and predict driver fatigue.…”

Section: Study Backgroundmentioning

confidence: 99%

Driver Fatigue Detection Systems Using Multi-Sensors, Smartphone, and Cloud-Based Computing Platforms: A Comparative Analysis

Abbas

Alsheddy

2020

Sensors

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Internet of things (IoT) cloud-based applications deliver advanced solutions for smart cities to decrease traffic accidents caused by driver fatigue while driving on the road. Environmental conditions or driver behavior can ultimately lead to serious roadside accidents. In recent years, the authors have developed many low-cost, computerized, driver fatigue detection systems (DFDs) to help drivers, by using multi-sensors, and mobile and cloud-based computing architecture. To promote safe driving, these are the most current emerging platforms that were introduced in the past. In this paper, we reviewed state-of-the-art approaches for predicting unsafe driving styles using three common IoT-based architectures. The novelty of this article is to show major differences among multi-sensors, smartphone-based, and cloud-based architectures in multimodal feature processing. We discussed all of the problems that machine learning techniques faced in recent years, particularly the deep learning (DL) model, to predict driver hypovigilance, especially in terms of these three IoT-based architectures. Moreover, we performed state-of-the-art comparisons by using driving simulators to incorporate multimodal features of the driver. We also mention online data sources in this article to test and train network architecture in the field of DFDs on public available multimodal datasets. These comparisons assist other authors to continue future research in this domain. To evaluate the performance, we mention the major problems in these three architectures to help researchers use the best IoT-based architecture for detecting DFDs in a real-time environment. Moreover, the important factors of Multi-Access Edge Computing (MEC) and 5th generation (5G) networks are analyzed in the context of deep learning architecture to improve the response time of DFD systems. Lastly, it is concluded that there is a research gap when it comes to implementing the DFD systems on MEC and 5G technologies by using multimodal features and DL architecture.

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“…Driving parameters in comparison with facial features and drivers physiological parameters is another widely used approach in detection of fatigue during driving. Karolinska sleepiness scale (KSS) is an ideal method for vehicle driving parameters which refers as questionnaire that depends on drivers to self-involvement and answers of drivers from the pre-set questionnaire [16,17,18]. Then, these answers tend to pass through KSS and results are generated from the KSS scale.…”

Section: Background Studymentioning

confidence: 99%

Robust Drowsiness Detection for Vehicle Driver using Deep Convolutional Neural Network

Saif¹,

Mahayuddin²

2020

IJACSA

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Drowsiness detection during driving is still an unsolved research problem which needs to be addressed to reduce road accidents. Researchers have been trying to solve this problem using various methods where most of these solution lacks behind in accuracy, real-time performance, costly, complex to build, and has a higher computational cost with low frame rate. This research proposes robust method for drowsiness detection of vehicle drivers based on head pose estimation and pupil detection by extracting facial region initially. Proposed method used frame aggregation strategy in case of face region cannot be extracted in any frame due to shortcomings, i.e. light reflection, shadow. In order to improve identification under highly varying lighting conditions, proposed research used cascade of regressors cutting edge method where each regression refers estimation of facial landmarks. Proposed method used deep convolutional neural network (DCNN) for accurate pupil detection to learn non linear data pattern. In this context, challenges of varying illumination, blurring and reflections for robust pupil detection are overcome by using batch normalization for stabilizing distributions of internal activations during training phase which makes overall methodology less influenced by parameter initialization. Proposed research performed extensive experimentation where accuracy rate of 98.97% was achieved using frame rate of 35 fps which is higher comparing with previous research results. Experimental results reveal the effectiveness of the proposed methodology.

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Real-time classification for autonomous drowsiness detection using eye aspect ratio

Cited by 124 publications

References 75 publications

Convolutional neural network model based on radiological images to support COVID-19 diagnosis: Evaluating database biases

Convolutional neural network model based on radiological images to support COVID-19 diagnosis: Evaluating database biases

Driver Fatigue Detection Systems Using Multi-Sensors, Smartphone, and Cloud-Based Computing Platforms: A Comparative Analysis

Robust Drowsiness Detection for Vehicle Driver using Deep Convolutional Neural Network

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