Design of a control and monitoring system to reduce traffic accidents due to drowsiness through image processing

Bruno Eraldo, Grimaldo Quispe, Heyul Chavez-Arias, Carlos Raymundo-Ibanez, Francisco Dominguez

Research output: Chapter in Book/ReportConference contributionpeer-review

Abstract

It is known that 33% of traffic accidents worldwide are caused by drunk driving or drowsiness [1] [2], so a drowsiness level detection system that integrates image processing was developed with the use of Raspberry Pi3 with the OpenCV library; and sensors such as MQ-3 that measures the percentage of alcohol and the S9 sensor that measures the heart rate. In addition, it has an alert system and as an interface for the visualization of the data measured by the sensors a touch screen. With the image processing technique, facial expressions are analyzed, while physiological behaviors such as heart rate and alcohol percentage are measured with the sensors. In image test training you get an accuracy of x in a response time of x seconds. On the other hand, the evaluation of the operation of the sensors in 90% effective. So the method developed is effective and feasible.

Original languageAmerican English
Title of host publication2019 IEEE 39th Central America and Panama Convention, CONCAPAN 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728108834
DOIs
StateIndexed - Nov 2019
Externally publishedYes
Event39th IEEE Central America and Panama Convention, CONCAPAN 2019 - Guatemala City, Guatemala
Duration: 20 Nov 201922 Nov 2019

Publication series

Name2019 IEEE 39th Central America and Panama Convention, CONCAPAN 2019
Volume2019-November

Conference

Conference39th IEEE Central America and Panama Convention, CONCAPAN 2019
Country/TerritoryGuatemala
CityGuatemala City
Period20/11/1922/11/19

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

Keywords

  • Image processing
  • Raspberry Pi 3
  • drowsiness
  • interface
  • sensor

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