International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 10 Issue: 05 | May 2023
p-ISSN: 2395-0072
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Stay Awake Alert: A Driver Drowsiness Detection System with Location Tracking and Alarm Dr. S.V. Sonekar1, Nikhil Charde2, Pragya Bagde3, Mohit Pantawane4, Ashish Kapse5 1Principal, Department of Computer Science J D College of Engineering and Management Nagpur, India
2345UG student, Department of Computer Science J D College of Engineering and Management Nagpur, India
---------------------------------------------------------------------***--------------------------------------------------------------------use physiological signals from the human body such as the Abstract - Drowsiness is a condition when a person feels
brain, eyes, and heart.This system uses signals such as EEG electroencephalography signals for the brain or EMG electromyography signals for muscle tone. This system has to be implemented with wearable devices which might make the driver uncomfortable because of wearability issues. Because of these issues in physical and physiological techniques in this system behavioural measures have been used for detecting driver drowsiness in the proposed research. It does not require any complex programming or costly components and due to its non-contact behaviour, the driver does not worry about wearability issues . Firstly a webcam is used for recording real-time video of the driver. the webcam is placed in front of the driver to continuously capture the image of the driver. The frames are extracted from the video using OpenCV.it is a real-time computer vision library. haar cascade algorithm used to detect faces from the frames. After face detection, facial landmarks have been extracted by using lib library. the facial landmarks are then used to compute the EAR eye aspect ratio. .after this convolutional neural network is used for classifying the state of the driver. the EAR value is compared with the threshold value that is taken as 0.2 in the proposed system if EAR value becomes less than the threshold value It is found that the drowsiness is detected as eyes are found to be closed. Then an alarm will be sent to alert the driver. After that the location of the driver will be shown to the driver.
the need to fall asleep. There are many reasons that can cause drowsiness which include lack of sleep, depression, working overtime, etc. which turns out to be dangerous in the form ofroad accidents if the drowsy person is a driver driving a vehicle. Studies reveal that a person is most likely to die from drowsy driving as compared to driving while consuming alcohol or being distracted while driving. This paper focuses on a real-time low-cost system that detects drowsiness using a machine-learning approach. the driver will be monitored continuously by using a webcam. openCV is used with the haar cascade algorithm for face detection, dlib is used to detect facial landmarks, and compute EAR eye aspect ratio to detect driver drowsiness based on the threshold value. Here CNN Convolutional neural network is used for determining the state of the driver whether the driver is drowsy or not.
Key Words: Dlib, Eye Aspect Ratio, opencv, Haar cascade algorithm, convolutional neural network, Machine learning.
1. INTRODUCTION As per reports from the Centers for Disease Control and Prevention[1].1 out of 25 adult drivers fall asleep while driving. According to the national sleep foundation around 6,400 people die yearly involving accidents caused by drowsiness. These accidents are not only dangerous for the driver but also for the passengers and the people who are using the road it can cause mental, physical, and financial damage. NHTSA National Highway Traffic Safety Administration reported that accidents related to drowsiness causing injury or death cost $109 billion yearly. Thus there is a need occur to develop a system that will keep the driver awake while driving.
Yann Lecun is the director of the Facebook AI research group [3] who built the first model of a Convolutional neural network in the year 1988. As the domain of computer vision is increasing day by day it is enabling machines to view the world as humans do. The amazing advancement in computer vision is because of machine learning, particularly with the convolutional neural network algorithm. Machine learning gives the machine the ability to learn and use this learning to perform various tasks.a convolutional neural network is used for detecting and classifying objects. Therefore to build our proposed system we have used machine learning with the convolutional neural network. Figure 1 shows the overall engineering of the system.
There are many techniques used for developing driver drowsiness detection system. [2] Vehicle-based drowsiness detection system in this technique drowsiness is detected by in-vehicle sensors collect data for detecting the drowsiness level of the driver through his behaviour the detection aspects are the steering wheel movement , vehicle deviation and position, and vehicle speed. This type of system requires costly infrastructure and complex programming. Physiological drowsiness detection systems
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