使用视觉行为和机器学习的驾驶员困倦监测系统

Strad Research Pub Date : 2021-07-12 DOI:10.37896/sr8.7/017
M. Aishwarya, Shampuram Shalini, P. Deepthi, Dr. V. Anantha Krishna
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引用次数: 4

摘要

当前位置疲劳驾驶是导致交通事故死亡的主要原因之一。卡车司机强迫不间断的长时间(特别是在晚上),机器司机长距离行驶或在一天内,汽车更容易出现这个问题。司机打瞌睡对每个国家的乘客来说都是一种极大的痛苦。在先进的系统中,每个人身上都有一个摄像头,通过图像处理的方式检测到录像带和驾驶者的面部信息。被检测的面部里程碑是明确的,最后计算眼睛长宽率、张嘴率和鼻子长率,并根据它们的值进行计数。瞌睡的检测主要完全依赖于先进的自适应阈值。很大的距离相当于闭上眼睛。然而,如果连续五帧不受限制地观察眼睛,机器就会吸引到驱动力正在入睡的完美状态,并发出警告信号。机器同样适合发现,而眼睛无法观察,并在负担得起的照明机构条件下工作。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
DRIVER DROWSINESS MONITORING SYSTEM USING VISUAL BEHAVIOUR AND MACHINE LEARNING
: Drowsy driving is one of the foremost reasons of deaths taking place in road accidents. The truck motorists who force for non- stop lengthy hours (in particular at night), machine motorists of lengthy distance course or in a single day, motorcars are lower prone to this problem. Motorist doziness is a heavy agony to passengers in each country. In the advanced system, a webcam information the videotape and motorist’s face are detected in every body using image processing ways. Facial milestones at the detected face are pointed and in the end the eye aspect rate, mouth opening rate and nose length rate are reckoned and counting on their values, Doziness is detected primarily rested completely on advanced adaptive thresholding. A massive distance corresponds to eye closure. However, the machine attracts the consummation that the driving force is falling asleep and troubles a caution signal, If the eyes are observed unrestricted for five successive frames. The machine is likewise suitable of discover whilst the eyes cannot be observed, and works below affordable lighting institutions conditions.
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