1 research outputs found
Driver Drowsiness Detection System: An Approach By Machine Learning Application
The majority of human deaths and injuries are caused by traffic accidents. A
million people worldwide die each year due to traffic accident injuries,
consistent with the World Health Organization. Drivers who do not receive
enough sleep, rest, or who feel weary may fall asleep behind the wheel,
endangering both themselves and other road users. The research on road
accidents specified that major road accidents occur due to drowsiness while
driving. These days, it is observed that tired driving is the main reason to
occur drowsiness. Now, drowsiness becomes the main principle for to increase in
the number of road accidents. This becomes a major issue in a world which is
very important to resolve as soon as possible. The predominant goal of all
devices is to improve the performance to detect drowsiness in real time. Many
devices were developed to detect drowsiness, which depend on different
artificial intelligence algorithms. So, our research is also related to driver
drowsiness detection which can identify the drowsiness of a driver by
identifying the face and then followed by eye tracking. The extracted eye image
is matched with the dataset by the system. With the help of the dataset, the
system detected that if eyes were close for a certain range, it could ring an
alarm to alert the driver and if the eyes were open after the alert, then it
could continue tracking. If the eyes were open then the score that we set
decreased and if the eyes were closed then the score increased. This paper
focus to resolve the problem of drowsiness detection with an accuracy of 80%
and helps to reduce road accidents