Driver Drowsiness Detection System using Machine Learning Algorithms
Shivani Sheth1, Aditya Singhal2, V.V. Ramalingam3
1Shivani, Sheth Computer Science and Engineering SRM Institute of Science and Technology Chennai, India.
2Aditya, Singhal Computer Science and Engineering SRM Institute of Science and Technology Chennai, India.
3V.V.Ramalingam, Computer Science and Engineering SRM Institute of Science and Technology Chennai, India.
Manuscript received on February 02, 2020. | Revised Manuscript received on February 10, 2020. | Manuscript published on March 30, 2020. | PP: 990-993 | Volume-8 Issue-6, March 2020. | Retrieval Number: F7514038620/2020©BEIESP | DOI: 10.35940/ijrte.F7514.038620
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Road crashes are the most common forms of accidents and deaths worldwide, and the significant reasons for these accidents are usually drunken, drowsiness and reckless behaviour of the driver. According to the World Health Organization, road traffic injuries have risen to 1.25 billion worldwide, which makes driver drowsiness detection a major potential area to avert numerous sleep-induced road accidents. This project proposes an idea to detect drowsiness using machine learning algorithms, hence alarming the driver in real-time to prevent a collision. The model uses the Haar Cascade algorithm, along with the OpenCV library to monitor the real-time video of the driver and to detect the eyes of the driver. The system uses the Eye Aspect Ratio (EAR) concept to determine if the eyes are open or closed. We also feed a data-set file consisting of the facial features data-points to train the machine learning algorithm. The model inspects each frame of the video, which helps to recognize the state of the driver. Furthermore, a Raspberry Pi single-board computer, combined with a camera module and an alarm system, facilitates the project to emulate a compact drowsiness detection system suitable for different automobiles.
Keywords: Raspberry Pi, Open CV, Haar Cascades, Image Processing, Eye Detection, Driver drowsiness, Alarm System, Real time.
Scope of the Article: Machine Learning.