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

doi:10.1109/CONCAPANXXXIX47272.2019.8976928

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

Facultad de Ingeniería

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

2 Citas (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 original	Inglés
Título de la publicación alojada	2019 IEEE 39th Central America and Panama Convention, CONCAPAN 2019
Editorial	Institute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)	9781728108834
DOI	https://doi.org/10.1109/CONCAPANXXXIX47272.2019.8976928
Estado	Publicada - nov. 2019
Evento	39th IEEE Central America and Panama Convention, CONCAPAN 2019 - Guatemala City, Guatemala Duración: 20 nov. 2019 → 22 nov. 2019

Serie de la publicación

Nombre	2019 IEEE 39th Central America and Panama Convention, CONCAPAN 2019
Volumen	2019-November

Conferencia

Conferencia	39th IEEE Central America and Panama Convention, CONCAPAN 2019
País/Territorio	Guatemala
Ciudad	Guatemala City
Período	20/11/19 → 22/11/19

ODS de las Naciones Unidas

Este resultado contribuye a los siguientes Objetivos de Desarrollo Sostenible

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10.1109/CONCAPANXXXIX47272.2019.8976928

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Eraldo, B., Quispe, G., Chavez-Arias, H., Raymundo-Ibanez, C., & Dominguez, F. (2019). Design of a control and monitoring system to reduce traffic accidents due to drowsiness through image processing. En 2019 IEEE 39th Central America and Panama Convention, CONCAPAN 2019 Artículo 8976928 (2019 IEEE 39th Central America and Panama Convention, CONCAPAN 2019; Vol. 2019-November). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CONCAPANXXXIX47272.2019.8976928

Eraldo, Bruno ; Quispe, Grimaldo ; Chavez-Arias, Heyul et al. / Design of a control and monitoring system to reduce traffic accidents due to drowsiness through image processing. 2019 IEEE 39th Central America and Panama Convention, CONCAPAN 2019. Institute of Electrical and Electronics Engineers Inc., 2019. (2019 IEEE 39th Central America and Panama Convention, CONCAPAN 2019).

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

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.",

keywords = "Image processing, Raspberry Pi 3, drowsiness, interface, sensor",

author = "Bruno Eraldo and Grimaldo Quispe and Heyul Chavez-Arias and Carlos Raymundo-Ibanez and Francisco Dominguez",

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year = "2019",

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Eraldo, B, Quispe, G, Chavez-Arias, H , Raymundo-Ibanez, C & Dominguez, F 2019, Design of a control and monitoring system to reduce traffic accidents due to drowsiness through image processing. En 2019 IEEE 39th Central America and Panama Convention, CONCAPAN 2019., 8976928, 2019 IEEE 39th Central America and Panama Convention, CONCAPAN 2019, vol. 2019-November, Institute of Electrical and Electronics Engineers Inc., 39th IEEE Central America and Panama Convention, CONCAPAN 2019, Guatemala City, Guatemala, 20/11/19. https://doi.org/10.1109/CONCAPANXXXIX47272.2019.8976928

Design of a control and monitoring system to reduce traffic accidents due to drowsiness through image processing. / Eraldo, Bruno; Quispe, Grimaldo; Chavez-Arias, Heyul et al.
2019 IEEE 39th Central America and Panama Convention, CONCAPAN 2019. Institute of Electrical and Electronics Engineers Inc., 2019. 8976928 (2019 IEEE 39th Central America and Panama Convention, CONCAPAN 2019; Vol. 2019-November).

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

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AB - 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.

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Eraldo B, Quispe G, Chavez-Arias H , Raymundo-Ibanez C, Dominguez F. Design of a control and monitoring system to reduce traffic accidents due to drowsiness through image processing. En 2019 IEEE 39th Central America and Panama Convention, CONCAPAN 2019. Institute of Electrical and Electronics Engineers Inc. 2019. 8976928. (2019 IEEE 39th Central America and Panama Convention, CONCAPAN 2019). doi: 10.1109/CONCAPANXXXIX47272.2019.8976928

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

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