International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Driver Drowsiness Detection using Machine Learning Approach

Author(s) Vikash Gupta
Country India
Abstract Driver fatigue is a major cause of traffic accidents. The number of deaths and injuries increases every year around the world. Traffic accidents can be reduced by detecting driver fatigue. This article describes machine learning for sleep detection. Face detection is used to detect the driver's eye area and use this as a reference for eye tracking in subsequent frames. Finally, visual images are used to detect sleep and a warning system is created. This method is divided into three stages: face detection, eye detection, and fatigue detection. Image processing is used to recognize the driver's face and then extract the image of the driver's eyes to detect fatigue. HAAR face detection algorithm outputs the image and then adjusts face detection based on the output. CHT is then used to track the eyes of the visible face. Check the eyes using EAR (Early Evaluation). The proposed system was tested using the proposed system on a Raspberry pi 3 Model B with 1 GB RAM using Logitech HD Webcam C270. According to some video tests, average eye contact and tracking accuracy can reach 95.0%. Therefore, it is a cheaper and better solution for a tired driver to ask to find the road immediately.
Keywords Haar Face detection, AdaBoost, EAR(EyeAspectRatio), Raspberrypi3
Field Computer
Published In Volume 6, Issue 1, January-February 2024
Published On 2024-01-20
Cite This Driver Drowsiness Detection using Machine Learning Approach - Vikash Gupta - IJFMR Volume 6, Issue 1, January-February 2024. DOI 10.36948/ijfmr.2024.v06i01.12245
DOI https://doi.org/10.36948/ijfmr.2024.v06i01.12245
Short DOI https://doi.org/gtfmqw
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