Vehicle Detection And Speed Detection Using OpenCV AND Haar Cascade

Abstract

Nowadays, accidents occur during drowsy road trips and increase day by day; It is
a known fact that many accidents occur due to driver fatigue and sometimes
inattention, this research is primarily devoted to maximizing efforts to identify
drowsiness. State of the driver under real driving conditions. The aim of driver
drowsiness detection systems is to try to reduce these traffic accidents. The
secondary data collected focuses on previous research on systems for detecting
drowsiness and several methods have been used to detect drowsiness or inattentive
driving. Our goal is to provide an interface where the program can automatically
detect the driver's drowsiness and detect it in the event of an accident by using the
image of a person captured by the webcam and examining how this information can
be used to improve driving safety can be used. . a vehicle safety project that helps
prevent accidents caused by the driver's sleep. Basically, you're collecting a human
image from the webcam and exploring how that information could be used to
improve driving safety. Collect images from the live webcam stream and apply
machine learning algorithm to the image and recognize the drowsy driver or not.
When the driver is sleepy, it plays the buzzer alarm and increases the buzzer . If,family members about their situation. Hence, this utility goes beyond the problem
of detecting drowsiness while driving. Eye extraction, face extraction with dlib.