Multivariate Approach to Alcohol Detection in Drivers by Sensors and Artificial Vision. Rosero-Montalvo, P., López-Batista, V., Peluffo-Ordóñez, D., Erazo-Chamorro, V., & Arciniega-Rocha, R. Volume 11487 LNCS , 2019. doi abstract bibtex © 2019, Springer Nature Switzerland AG. This work presents a system for detecting excess alcohol in drivers to reduce road traffic accidents. To do so, criteria such as alcohol concentration the environment, a facial temperature of the driver and width of the pupil are considered. To measure the corresponding variables, the data acquisition procedure uses sensors and artificial vision. Subsequently, data analysis is performed into stages for prototype selection and supervised classification algorithms. Accordingly, the acquired data can be stored and processed in a system with low-computational resources. As a remarkable result, the amount of training samples is significantly reduced, while an admissible classification performance is achieved - reaching then suitable settings regarding the given device’s conditions.
@book{
title = {Multivariate Approach to Alcohol Detection in Drivers by Sensors and Artificial Vision},
type = {book},
year = {2019},
source = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
keywords = {Alcohol detection,Drunk detection,Prototype selection,Sensors,Supervised classification},
volume = {11487 LNCS},
id = {855f2a43-37db-3e07-8692-afb9923fd382},
created = {2019-06-01T23:59:00.000Z},
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last_modified = {2020-12-23T13:54:30.368Z},
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abstract = {© 2019, Springer Nature Switzerland AG. This work presents a system for detecting excess alcohol in drivers to reduce road traffic accidents. To do so, criteria such as alcohol concentration the environment, a facial temperature of the driver and width of the pupil are considered. To measure the corresponding variables, the data acquisition procedure uses sensors and artificial vision. Subsequently, data analysis is performed into stages for prototype selection and supervised classification algorithms. Accordingly, the acquired data can be stored and processed in a system with low-computational resources. As a remarkable result, the amount of training samples is significantly reduced, while an admissible classification performance is achieved - reaching then suitable settings regarding the given device’s conditions.},
bibtype = {book},
author = {Rosero-Montalvo, P.D. and López-Batista, V.F. and Peluffo-Ordóñez, D.H. and Erazo-Chamorro, V.C. and Arciniega-Rocha, R.P.},
doi = {10.1007/978-3-030-19651-6_23}
}
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