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Drowsiness Detection to Prevent Accidents

Authors: Sangameshwar; Dharani V; Anirudh Reddy M; Gotlluri Jaswanthi;

Drowsiness Detection to Prevent Accidents

Abstract

In the current era, 60% of the accidents occur due to drowsiness. Drowsiness mainly occurs during early morning and during night times. This kind of road accident can cause a lot of harm to other people and government roads and other properties. These accidents finally lead to high injuries and death. To prevent this kind of accident and to save the lives of people, a lot of research is still going on.There are many kinds of methods to prevent this. But, everyone can't afford it or adjust to the systems environment. Though there are many systems none is able to provide accurate action, speed, sound, and no failure. In this paper, we designed the system in such a way that drivers won't get distracted, it is affordable, easy to fit in car environment and accurate results. In our proposed system when drivers feel dizzy and when he/she closes their eyes a huge beep sound will be produced by using text speech. We do all this with the latest technology of image processing under iot. We use raspberry pi which helps in producing accurate results. A high range of IR cameras will be set up in front of the driver, to monitor drivers facial expression and to calculate the eye ration. As mentioned drowsiness occurs during dusk and dawn IR camera helps in monitoring the face during even at darkness. This system is highly affordable, easy to access, easy implementation and accurate results.

Keywords

D6674049420/2020©BEIESP, 2249-8958, This kind of road accident can cause a lot of harm to other people and government roads and other properties. These accidents finally lead to high injuries and death.

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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