A Study on Tiredness Measurement using Computer Vision

Abstract

In this paper, we studied tiredness measurement based on several different detection methods in real time. We know that the driver tiredness is one of the major causes of traffic accidents. So tiredness detection can play a vital role for preventing road accidents. By developing an automatic solution for alerting drivers of tiredness before an accident occurs, this could reduce the number of traffic accidents. The Haar-cascade classifier is exploited based on Haar-like features to find the eyes. The main purpose of the Haar-cascade classifier is to classify closed or open state of the eyes. If we can notice that the eyes are closed for a predefined span of time, we consider the state of the eyes can be closed. Based on this closed-state of the eye, a notification (like alarm) is initiated to alert. We have detected only right eye for saving processing load on the system. The reason is that when a person closes his eyes he usually does not close one eye, but both eyes at the same time. Several steps are taken into account for this system; we first capture the frame from the webcam. Then we need to detect face as well as eye. To detect blinks, we process ROI (region of image) of pupil area. Our result is found to be satisfactory.

A review of machine learning and deep learning algorithms for Parkinson's disease detection using handwriting and voice datasets

“…The subfields of artificial intelligence (AI), ML and DL are dedicated to developing algorithms and models capable of learning from data and making informed judgments or predictions [ 4 ]. The ML algorithms can be cate-gorized into three main types: supervised learning, unsupervised learning and reinforcement learning [ 5 ].…”