Driver drowsiness monitoring system using visual behaviour and machine learning

Kumar, Ashish; Patra, Rusha

doi:10.1109/iscaie.2018.8405495

Cited by 78 publications

(20 citation statements)

References 9 publications

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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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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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“…pr ← pr + g(Kα i ) Calculate the prediction end for err = y − pr Calculate the prediction error while err < tr, itr < tr itr do (19) end for for i=1:M do pr ← pr + g(Kα i ) Calculate the prediction end for err = y − pr Calculate the prediction error end while where x q p (t) is the limited-band signal within a frequency band q and K is the corresponding epoch length. In this study, we use the five conventional frequency bands of EEG signals including δ 1 (0.2-0.8 Hz), δ 2 (0.8-1.6 Hz), δ 3 (1.6-2.8 Hz), θ 1 (2.8-6.2 Hz), and α 1 (6.2-10 Hz).…”

Section: Narrow Band Spectral Analysis Of Band-limited Signals For Drmentioning

confidence: 99%

“…The questionnaires are usually filled before and after a tedious task where the performance differences can reveal the extent of the fatigue state. (b) video based techniques that utilize such physical symptoms as yawning, pattern motion of eyelid, eye, and head as well as facial and eye expression [17,18,19,20]. (c) cognitive tasks that use the reaction time or the error rate of the responses to a set of visual stimuli [21,22,23].…”

Section: Introductionmentioning

confidence: 99%

Sequential Nonlinear Encoding; A Low Dimensional Regression Algorithm with Application to EEG based Driving Fatigue Detection

Tabejamaat

Mohammadzade

2020

Scientia Iranica

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Background: Regression analysis of real world data has not always been an easy task, especially when input vectors are presented in a very low dimensional space. EEG based fatigue detection is one such low dimensional problems, playing a major role in reducing the risk of fatal accidents. New Method: We introduce a novel Regularized Kernel Projection Pursuit Regression method which is a two-step nonlinearity encoding algorithm tailored for such very low dimensional problems as fatigue detection. This way, the data nonlinearity can be investigated from two different perspectives, first by transforming the data into a high dimensional intermediate space and then by using their spline estimations to the output variables which allows for a hierarchical unfolding of data. Result: Experimental results on the SEED VIS database shows an average RMSE value of 0.1080% and 0.1054% respectively for the temporal and posterior areas of the brain. Our method is also validated by conducting some experiments on Parkinson's disease prediction which further demonstrate the efficiency of our method. Conclusion: This paper proposes a novel regression algorithm to address the encoding problem of a highly complex low dimensional data, which is usually encountered in bio-neurological prediction tasks like EEG based driving fatigue detection.

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“…The proposed model achieved an accuracy of 89.5% on 3-class classification and speed of 14.9 frames 3 per second (FPS) on Jetson TK1. In the paper "Driver Drowsiness Monitoring System using Visual Behaviour and Machine Learning", [2] the authors Ashish Kumar and Rusha Patra propose a low cost, real time driver's drowsiness detection system is developed with acceptable accuracy. Their findings lead to the conclusion that Bayesian classifiers give lesser accuracy as compared to SVM and hence using SVM is more beneficial than any other classifier.…”

Section: Related Workmentioning

confidence: 99%

Real-Time Fatigue Detection System using Computer Vision

Patel¹

2020

IJERT

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Fatigue can lead to low productivity and can also cause accidents in case of drivers. According to our study, some work has been done for detection of drowsiness. The research aims to detect the onset onset of fatigue in people etc. which will also help detect a certain disease which have fatigue as symptoms. This paper proposes a Fatigue Detection System. Our system will work on Computer Vision. Our main focus is on the person's facial expressions. Our system will have five stages-Face detection, Facial landmark detection, Feature extraction, Eye closure status, Lip opening status.

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Driver drowsiness monitoring system using visual behaviour and machine learning

Cited by 78 publications

References 9 publications

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

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

Sequential Nonlinear Encoding; A Low Dimensional Regression Algorithm with Application to EEG based Driving Fatigue Detection

Real-Time Fatigue Detection System using Computer Vision

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