Driver Drowsiness Detection and Alert System

Abstract

Driver drowsiness is a significant factor in road accidents, necessitating the development of effective detection and alert systems to mitigate this risk. This paper provides a comprehensive review of driver drowsiness detection and alert systems, examining the technologies, methodologies, and challenges associated with these systems. Our goal is to provide an interface where the program can automatically detect the driver\'s drowsiness and detect it in the event of an accident by using the image of a person captured by the webcam and examining how this information can be used to improve driving safety can be used. . a vehicle safety project that helps prevent accidents caused by the driver\'s sleep. Basically, you\'re collecting a human image from the webcam and exploring how that information could be used to improve driving safety. Collect images from the live webcam stream and apply machine learning algorithm to the image and recognize the drowsy driver or not. Despite advancements, challenges remain in achieving high accuracy and minimizing false alarms. Future directions include improving sensor technology, enhancing algorithm robustness, and addressing user acceptance issues. Overall, driver drowsiness detection and alert systems play a crucial role in enhancing road safety and warrant continued research and development efforts.

Introduction

I. INTRODUCTION

Driver drowsiness poses a significant threat to road safety, contributing to a substantial number of accidents worldwide. The impairment of cognitive functions and reaction times due to fatigue increases the risk of accidents, endangering the lives of drivers, passengers, and pedestrians. In response to this pressing issue, driver drowsiness detection and alert systems have emerged as vital technologies aimed at preventing accidents caused by driver fatigue.

Face and brand recognition is used with the help of image processing of facial images captured by the camera to identify distractions or drowsiness. To solve the problem, we came up with the implemented solution in the form of image processing. Perform image editing. , OpenCV and Dlib open source libraries are used. Python is employed because the language to implement the idea. associate degree infrared camera is used to endlessly track the driving force' facial markings and eye movements. This project mainly focuses on the driver's eye markings. Driver. Eye characteristics are continuously tracked to detect drowsiness. Images are captured by the camera, these images are forwarded to an image processing module that performs face recognition to detect distraction and drowsiness of the driver. sThe following use cases are covered in this project. If the driver's eyes are closed for a limited period of time, the driver is considered drowsy and the corresponding audible alarm is used to warn the driver. II. METHODS AND MATERIAL Tools & Image Processing Methods Open CV: OpenCV (Open-Source Computer Vision) is the Swiss Army Knife of Computer Vision, it has a wide range of modules that can help us with many Computer Vision problems, but perhaps the most useful part of OpenCV is its architecture. and memory management. It gives you a framework in which to work with pictures and videos however you want, using OpenCV algorithms or your own, without worrying about allocating and reallocating memory for your pictures. optimized and can be used for real-time video and image processing The highly optimized image processing function of OPENCV is used by the author for real-time image processing of live video streaming from the camera.. First, the paper outlines the scope of the problem, emphasizing the significant impact of driver drowsiness on road safety and the associated economic and societal costs. It discusses the physiological and behavioral manifestations of drowsiness, such as yawning, drooping eyelids, and impaired concentration, which compromise driving performance and increase the likelihood of accidents.Next, the introduction introduces the concept of driver drowsiness detection and alert systems as technological solutions to this problem. It delineates the primary objectives of these systems: to monitor driver behavior, identify signs of drowsiness, and issue timely alerts to prevent accidents. By leveraging advancements in sensor technology, signal processing, and machine learning, these systems aim to enhance situational awareness and assist drivers in maintaining vigilance behind the wheel.

Furthermore, the introduction provides an overview of the structure and organization of the paper. It outlines the subsequent sections, which will delve into the various aspects of driver drowsiness detection and alert systems, including sensor technologies, signal processing techniques, alert generation mechanisms, integration with vehicle systems, performance evaluation methodologies, and future directions.

DLib: Dlib is a modern C toolkit with algorithms and tools for machine learning to create complex C ++ software to solve real problems. It is used in a wide variety of fields in both industry and academia, including robotics, embedded devices, cell phones, and large, high-performance computing environments. Lib's open source licenses allow you to use it in any application for free.The author uses the open source Dib library for the CNN (Neural Networks) implementation. The author uses highly optimized prediction functions and detectors of previously learned face shapes to detect facial features. EAR (Eye Aspect Ratio) The numerator of this equation calculates the distance between the vertical landmarks of the eye, while the denominator denotes. calculates distance between the horizontal eye reference points, weighting the denominator accordingly since there is only one. The aspect ratio of the eye is roughly constant when the eye is open, but quickly drops to zero when you blink.When the person blinks, the aspect ratio of the eyes drops dramatically and approaches zero. As shown in Figure 2, the aspect ratio of the eyes is constant, then quickly drops to zero and then increases again, suggesting that a single blink has occurred.

Algorithm Steps

Step 1 – Take image as input from a camera. Step 2 – Recognize the face in the image and create a region of interest (ROI). Step 3 – Recognize the eyes from the ROI and send them to the classifier Step 4 – The classifier classifies whether the eyes are open or closed Step 5 – Calculate the score to be verified. when the person is sleepy

Conclusion

The model is capable of detecting drowsiness by monitoring the eyes and mouth. Shape prediction methods are used to detect important features on the face. The inputs given to these methods are facial and marks which are obtained from facial landmark detection. This module deals with the EAR function which computes the ratio of distances between the horizontal and vertical eye landmarks. An deployed which is used for giving appropriate alerts when the driver is feeling drowsy. The whole project is designed to decrease the rate of accidents and to contribute to the technology with the goal to prevent fatalities caused due to road accidents. In the future, more work can be done to automate the zoom on the eyes after they are localized. drowsiness. The outer factors may be weather conditions, state of the vehicle, time of sleeping and, mechanical data. Driver drowsiness is among the major threats to road safety, and in the case of commercial motor vehicle operators, the problem is particularly severe. The factors that contribute to this serious safety issue aretwenty-four-hour services, unpredictable conditions of the environment, high annual mileage, and an increase of work schedules that are demanding. One important step of preventive measures that are needed to solve this problem is by continuously observing the driver’s drowsiness state and giving information about their state to the driver so that they can take necessary action. Presently, no adjustment can be done concerning the zoom or direction of the camera during the system operation. In the future, more work can be done to automate the zoom on the eyes after they are localized.

References

[1] Rahul Atul Bhope, “Computer Vision based drowsiness detection formotorized vehicles with Web Push Notifications”, IEEE 4thInternational Conference on Internet of Things, IEEE, Ghaziabad,India,2019. [2] Jasper S. Wijnands,Jason Thompson,Kerry A. Nice,Gideon D. P,Aschwanden &Mark Stevenson, “Real-time monitoring of driverdrowsiness on mobile platforms using 3D neural networks”, NeuralComputing and Applications, 2019. [3] ChrisSchwarz,JohnGaspar,ThomasMiller&RezaYousefian,“Thedetectionofdrowsinessusingadrivermonitoringsystem”,inJournalof Traffic Injury Prevention (Taylor and Francis Online), 2019. [4] Aditya Ranjan, Karan Vyas, Sujay Ghadge, Siddharth Patel, Journal of Engineering andTechnology(IRJET), 2020. [5] P. Olivier. A Framework for Debugging Heterogeneous Applications. Phd Thesis, University of Amsterdam, December 2000. [6] M.G.J. van den Brand, A. Van Deursen, J. Heering, H.A. de Jong, M. De Jonge, T. Kuipers, P. Klint, L. Moonen, P.A. Olivier, J. Scheerder, J.J. Vinju, E. Visser, and J. Visser, The ASF + SDF Meta-Environment: a Component-Based Language Development Environment. In: Proceedings of Compiler Construction (CC\'01). [7] Simxmd: Integrated debugging of C code and hardware components, author Ruediger Willenberg, Paul Chow 2012, International Conference on Field Programmable Technology . [8] A Study of Heart Rate and Brain System Complexity and Their Interaction in Sleep Deprived Subjects. Kokonozi A.K., Michail E.M., Chouvarda I.C., Maglaveras N.M. Bologna, Italy. : Computers in Cardiology, 2008. [9] Effects of partial and total sleep deprivation on driving performance. Peters R.D., Wagner E., Alicandri E., Fox J.E., Thomas M.L., Thorne D.R., Sing H.C., Balwinski S.M. 1999. [10] Effect of driving duration and partial sleep deprivation on subsequent alertness and performance of car drivers. Otmani S, Pebayle T, Roge J, Muzet A. 2005. [11] TemplateMatching. [Online] Apr 21, 2014 [Cited: Sep 8, 2014.] Real time drowsy driver detection using haarcascade samples. Dr.Suryaprasad J, Sandesh D, Saraswathi V. [12] Integrated Approach for Nonintrusive Detection of Driver Drowsiness. Yu, Xun. Duluth : s.n., 2012

Copyright

Copyright © 2024 Abhishek Patle, Jayesh Gotephode, Mohit Lanjewar, Samiksha Gedam, Prof. Anuja Ghasad. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.