An Effective Model to Alert a Drowsy Driver using Eye Closure Rate
Pavan Kumar Tadiparthi1, Venkata Suma Priya Kankipati2, Jessie Alekhya Kandimalla3, Priyanka Gottimukkala4, Pradeep Kumar Bheemavarapu5
1Pavan Kumar Tadiparthi, Associate Professor in the department of information Technology, MVGR College of Engineering, Vizianagaram, Andhra Pradesh, India.
2Venkata Suma Priya Kankipati, under graduate in Information Technology, MVGR College of Engineering, Vizianagaram, Andhra Pradesh, India.
3Jessie Alekhya Kandimalla, Under Graduate in Information Technology, MVGR College of Engineering, Vizianagaram, Andhra Pradesh, India
4Priyanka Gottimukkala, Under Graduate in Information Technology, MVGR College of Engineering, Vizianagaram, Andhra Pradesh, India.
5Pradeep Kumar Bheemavarapu, Under Graduate in Information Technology, MVGR College of Engineering, Vizianagaram, Andhra Pradesh, India
Manuscript received on February 10, 2020. | Revised Manuscript received on February 20, 2020. | Manuscript published on March 10, 2020. | PP: 2409-2412 | Volume-9 Issue-5, March 2020. | Retrieval Number: E2671039520/2020©BEIESP | DOI: 10.35940/ijitee.E2671.039520
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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: Transportation plays a major role in today’s world. To move from one place to another place (long distances) which cannot be covered by walk, we use vehicles which consumes less time to reach destination. According to statistics, by 2050 the urban population will increase by 68% which leads to an increase in transportation that causes pollution and increase in the rate of road accidents. There are many methods and prevention measures to control pollution. The road accidents are caused due to distracted driving, high speed, drowsy driving and disobeying traffic rules. Among these, drowsy driving has been a cause for 20% of road accidents which is because of fatigue driving. In this article, a model is proposed based on image processing technique which is segmentation and a deep convolutional neural network architecture to improve the performance of the model when compared to the existing models. The proposed model works with better performance in different lighting conditions.
Keywords: Image Segmentation, Drowsiness, Convolutional Neural Network, Haar feature selection, driver fatigue.
Scope of the Article: Artificial Intelligent Methods, Models, Techniques