Implementation of Haar Cascade Classifier and Eye Aspect Ratio for Driver Drowsiness Detection Using Raspberry Pi

Driver Drowsiness Detection by Identification of Yawning and Eye Closure

Today most accidents are caused by drivers’ fatigue, drowsiness and losing attention on the road ahead. In this paper, a system is introduced, using RGB-D cameras to automatically identify drowsiness and give warning. In this system two important modules have been utilized simultaneously to identify the state of driver’s mouth and eyes for detecting drowsiness. At first, using the depth informa...

Object detection using Haar-cascade Classifier

Object detection is an important feature of computer science. The benefits of object detection is however not limited to someone with a doctorate of informatics. Instead, object detection is growing deeper and deeper into the common parts of the information society, lending a helping hand wherever needed. This paper will address one such possibility, namely the help of a Haar-cascade classifier...

Automatic Solar Cavity Detection Using Haar Cascade Classifier

Solar physicists have recently been focusing on the examination of solar cavities, circular structures appearing on the sun's upper atmosphere. The high volume of continuously received solar data from observing spacecraft make it difficult to find and identify solar cavities. In this study, we developed a computer model for automated detection of solar cavities using Haar cascade classifiers. O...

Design and implementation of driver drowsiness detection system

Driver Drowsiness Detection System Using Image Processing

Drivers who do not take regular breaks when driving long distances run a high risk of becoming drowsy a state which they often fail to recognize early enough according to the experts. Studies show that around one quarter of all serious motorway accidents are attributable to sleepy drivers in need of a rest, meaning that drowsiness causes more road accidents than drink-driving. Attention assist ...