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

Producción científica: Libro o Capítulo del libro Contribución a la conferenciarevisión exhaustiva

1 Cita (Scopus)

Resumen

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.

Idioma originalInglés estadounidense
Título de la publicación alojada2019 IEEE 39th Central America and Panama Convention, CONCAPAN 2019
EditorialInstitute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)9781728108834
DOI
EstadoIndizado - nov. 2019
Publicado de forma externa
Evento39th IEEE Central America and Panama Convention, CONCAPAN 2019 - Guatemala City, Guatemala
Duración: 20 nov. 201922 nov. 2019

Serie de la publicación

Nombre2019 IEEE 39th Central America and Panama Convention, CONCAPAN 2019
Volumen2019-November

Conferencia

Conferencia39th IEEE Central America and Panama Convention, CONCAPAN 2019
País/TerritorioGuatemala
CiudadGuatemala City
Período20/11/1922/11/19

Nota bibliográfica

Publisher Copyright:
© 2019 IEEE.

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