Drowsy Driver Detection using MSER Feature Detection and Binarization on MATLAB | Zendy

Asma Jamesh | Zendy

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Drowsy Driver Detection using MSER Feature Detection and Binarization on MATLAB

Author(s) -

Asma Jamesh

Publication year - 2021

Publication title -

aijr proceedings

Language(s) - English

Resource type - Conference proceedings

ISSN - 2582-3922

DOI - 10.21467/proceedings.114.31

Subject(s) - artificial intelligence , thresholding , computer vision , computer science , matlab , feature (linguistics) , rgb color model , haar like features , feature extraction , quadcopter , face detection , image (mathematics) , pattern recognition (psychology) , facial recognition system , engineering , linguistics , philosophy , aerospace engineering , operating system

Every year, 1.5 lakh people die in road mishaps in India. Among these accidents, 40% are due to ‘Drowsy or Sleep Driving’. According to several statistics, almost all commercial private drivers tend to drive continuously for 10 hours a day. Nearly all road accidents caused due to lack of sleep and drowsiness are highly hazardous and fatal. Drowsy Driver Detection Algorithm acquires real-time video and captures snapshots using an external Webcam. Using the Viola-Jones Algorithm, the face and the eyes of the driver are detected. The original RGB eye image is converted to a Gray image and then into a Binary image. Two techniques, Maximally Stable Extremal Regions Feature Detection and Binarization are deployed to determine the status of the driver. This research paper focuses on the development of a MATLAB algorithm to alert the driver or the co-passenger on-time by plotting the MSER Features and thresholding the acquired real-time images.

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