Driver Drowsiness Detection System with OpenCV and Keras
Abstract
Drowsiness and Fatigue of driver’s square measure amongst the many causes of
road accidents. Every year, the amounts of deaths increases and fatalities injuries
globally. During this paper, a module for Advanced Driver help System (ADAS) is
given to cut back the quantity of accidents because of driver’s fatigue and thus
increase the transportation safety; this technique deals with automatic driver
somnolence detection supported visual info and computing. we tend to propose
associate degree algorithmic program to find, track, and analyze each the drivers
face and eyes to live PERCLOS, a scientifically supported live of somnolence related
to slow eye closure. However, the development of such systems encounters many
difficulties related to fast and proper recognition of a driver’s fatigue symptoms. One
of the technical possibilities to implement driver drowsiness detection systems is to
use the visionbased approach. This article presents the currently used driver
drowsiness detection systems. The technical aspects of using the vision system to
detect a driver drowsiness are also discussed. The parameters of the eyes and
mouth detection are created within the face image. The video were change into
images frames per second. From there, locating the eyes and mouth can be
performed. Once the eyes are located, measuring the intensity changes in the eye
area determine the eyes are open or closed. If the eyes are found closed for 4
consecutive frames, it is confirm.
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