A Drowsiness Detection Scheme Based on Fusion of Voice and Vision Cues (original) (raw)
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Sensors
The amount of road accidents caused by driver drowsiness is one of the world’s major challenges. These accidents lead to numerous fatal and non-fatal injuries which impose substantial financial strain on individuals and governments every year. As a result, it is critical to prevent catastrophic accidents and reduce the financial burden on society caused by driver drowsiness. The research community has primarily focused on two approaches to identify driver drowsiness during the last decade: intrusive and non-intrusive. The intrusive approach includes physiological measures, and the non-intrusive approach includes vehicle-based and behavioral measures. In an intrusive approach, sensors are used to detect driver drowsiness by placing them on the driver’s body, whereas in a non-intrusive approach, a camera is used for drowsiness detection by identifying yawning patterns, eyelid movement and head inclination. Noticeably, most research has been conducted in driver drowsiness detection met...
Human Driver's Drowsiness Detection System
IRJET, 2022
An Advancements in technology and artificial intelligence in the past few years have led to improvements in driver monitoring systems. Many studies have collected real driver drowsiness data and applied machine learning algorithms to enhance the performance of these systems. This paper presents a review report on the project to develop a system for driver drowsiness detection to prevent accidents caused by driver fatigue. This paper contains reviews of recent systems using different methods to detect drowsiness. In this paper, the proposed system captures video of the driver's face to detect drowsiness and alert an alarm if needed. A machinelearning algorithm was applied to the model to evaluate the accuracy of this approach. Real-world implementation of the project gives an idea of how the system works and what can be done to improve the accuracy of the overall system. Furthermore, the paper highlights the observation, accuracy, and challenges of the system.
A Systematic Review of Driver Drowsiness Detection using Various Approaches
Drowsiness is one of the leading causes of road accidents, hence a monitoring system is required to identify drowsiness. Driver monitoring systems typically detect three sorts of data: biometric, vehicle, and driver graphic. Nowadays, several devices including navigation systems and warning alarm systems are available to help drivers. The human mistake causes numerous traffic fatalities and injuries worldwide. Drowsiness and mapping while driving is widely recognized as contributing factors to deadly car accidents. This article reviews several sleepiness detecting methods. The characteristics of these approaches are categorized and contrasted. One of them is computer vision-based picture processing. It utilizes the driver's eyes and facial gestures to identify tiredness. This survey study focuses on this strategy.
Driver Drowsiness Detection Using Non-Intrusive Technique
2017
Development of safety features to prevent drowsy driving is one of the major technical challenges in the automobile industry. Driving while in drowsy state is a major reason behind road accidents especially in the modern age. Driving when drowsy leads to a higher crash risk than being in alert state. Therefore, by using assistive systems to monitor driver’s level of alertness can be significant to help prevention of accidents. This paper aims towards detection of drivers drowsiness using visual features approach.Driver drowsiness is detected in real time by detecting drivers face and eyes using HAAR-Cascade Classifier and Yawn detection based on Template matching. The system will provide an alert to the driver if the driver is found to be in drowsy state with help of an alarm.
Sensors (Basel, Switzerland), 2017
Driver drowsiness is a major cause of fatal accidents, injury, and property damage, and has become an area of substantial research attention in recent years. The present study proposes a method to detect drowsiness in drivers which integrates features of electrocardiography (ECG) and electroencephalography (EEG) to improve detection performance. The study measures differences between the alert and drowsy states from physiological data collected from 22 healthy subjects in a driving simulator-based study. A monotonous driving environment is used to induce drowsiness in the participants. Various time and frequency domain feature were extracted from EEG including time domain statistical descriptors, complexity measures and power spectral measures. Features extracted from the ECG signal included heart rate (HR) and heart rate variability (HRV), including low frequency (LF), high frequency (HF) and LF/HF ratio. Furthermore, subjective sleepiness scale is also assessed to study its relati...