Real Time Driver Drowsiness Detection System

Drowsy driving is one of the leading causes of traffic accidents all over the world. Driving in a monotonous manner for an extended amount of time without stopping causes tiredness and catastrophic accidents. Drowsiness has the potential to ruin many people’s lives. As a result, a real-time system that is simple to create and configure for early and accurate sleepiness detection is required. In this study, a real-time vision-based system called Driver Drowsiness Detection System has been developed utilizing machine learning. In this study, the Haar Cascade classifier was used to recognize the driver’s face characteristics and functions present in OpenCV library to detect the region of the face. The following step is to examine the open/close state of the eyes, followed by sluggishness depending on the sequence of ocular conditions. The non-intrusive and cost-effective nature of this vision-based driver tiredness detection is its distinguishing attribute.

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“…Yawning Technique [17]: One of the signs of exhaustion is yawning. It is believed that yawning is modeled as a wide open mouth.…”

Section: Related Workmentioning

confidence: 99%

“…When you yawn, your mouth is wider than when you speak. Yawning [17] can be detected by monitoring the face and then tracking the mouth. The speed at which the mouth opens and the degree of change in the mouth contour area are used to identify yawns on paper, and when yawn is detected the driver is warned.…”

Section: Related Workmentioning

confidence: 99%

Driver Drowsiness Detection System Using Machine Learning

Thakur

Shivam

Raj

et al. 2022

Advances in Transdisciplinary Engineering

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“…Model A is the basic once, where it has no inbuilt ethernet compared to model b, it has two extra USB port and micro-SD card slot. [13] The figure 9 shows the raspberry pi 3 model b+. Broadcom BCM2835 on the chip that system on chip or sac is integrated circuit, which integrates all the components of a system into single system.…”

Section: A Raspberry Pi 3 Model B+mentioning

confidence: 99%

Raspberry Pi 3 Model B+ Based Endure Redicting Using Web of Things (Wot)

Rao¹,

Reddy²

2022

IJIRCST

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The Web of Things (WoT) is a breakthrough technology that connects and controls the devices with the help of Web and make them smart and efficient. We build an WoT based Temperature and Humidity Sensing System using Raspberry Pi 3 Model B+. Endure is a vital feature in customary life, which plays a significant part in many deeds. Therefore, occasionally we need to know Endure parameter instantly. Raspberry Pi is a miniature standalone system with multiple functionalities were executed. You will interface a Digital Temperature and Humidity Sensor to receive regular updates about the Temperature and Humidity of a location remotely. The Sensor is connected to Raspberry Pi 3 Model B+, which you will program to send the readings to the Cloud Server through web, which in turn send update to Thing Speak. Thing Speak is an application platform, where the data is sent to the sever for live monitoring from anywhere in the world over web. Email notification to the user at intervals. The project deals about the Temperature and Humidity values, to read and monitor the values, using ThingSpeak system via Raspberry Pi 3 Model B+.

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“…In the last step, we count then several white and black pixels and compare the distance between these coordinates for recognition of the driver's fatigue. A.N.Shewale et al [6] designed a system that will monitor the open or closed state of drivers' eyes in real-time. The video camera is placed on a car desk in front of the driver for monitoring the eyes state of the driver.…”

Section: Literature Surveymentioning

confidence: 99%

A Survey on e-Sarthi: Your Safety our Concern

Shelke¹

2020

IJRASET

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The majority of road accidents happen because of driver behavior and fault. Vehicle accidents not only cause death and injuries, but they are also reasons for financial losses and low productivity. The careless behavior of the driver also puts other drivers at risk. Driver drowsiness and have become important factors in accidents. So there is a need for the system that detects the drowsiness of the driver and alerts him. This paper gives a survey of the techniques used by the researcher for detecting driver's behavior in real-time while driving and abstract view of the proposed system that we are going to implement to analyze the driver's behavior by detecting drowsiness and yawning.

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Real Time Driver Drowsiness Detection System

Cited by 7 publications

References 2 publications

Driver Drowsiness Detection System Using Machine Learning

Driver Drowsiness Detection System Using Machine Learning

Raspberry Pi 3 Model B+ Based Endure Redicting Using Web of Things (Wot)

A Survey on e-Sarthi: Your Safety our Concern

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