Driver-Assistant System Using Computer Vision and Machine Learning
Safety has been one of the key points in vehicle design, in this case one of its main objectives is to implement warning systems to notify the driver about inappropriate or atypical process in their driving process, trying to avoid accidents that affect their vehicle passengers, as well as inflictin...
| Main Authors: | , , |
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| Format: | Online |
| Language: | spa |
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Universidad Pedagógica y Tecnológica de Colombia
2020
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| Online Access: | https://revistas.uptc.edu.co/index.php/ingenieria/article/view/11760 |
| _version_ | 1801706089855582208 |
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| author | Valencia-Payan, Cristian Muñoz-Ordóñez, Julián Pencue-Fierro, Leonairo |
| author_facet | Valencia-Payan, Cristian Muñoz-Ordóñez, Julián Pencue-Fierro, Leonairo |
| author_sort | Valencia-Payan, Cristian |
| collection | OJS |
| description | Safety has been one of the key points in vehicle design, in this case one of its main objectives is to implement warning systems to notify the driver about inappropriate or atypical process in their driving process, trying to avoid accidents that affect their vehicle passengers, as well as inflicting damage on third parties. Day by day, more systems are created to monitor the environment around the vehicle in order to ensure safe driving at all times. According to the World Health Organization, for 2016 there were 1.35 million deaths related to traffic accidents. This research presents the first driving assistance system developed for Colombia, the system detects and recognizes preventive and regulatory traffic signals and its precision is not affected by rotations and scale of the traffic signals present in an actual route, this is this way because the system is based on Haar classifiers. The system recognizes lane deviations, estimation of the curve direction and obstacle protruding along the way using computer vision algorithms, making it a low-cost computational system. Furthermore, this research provides the first resulting cascades for the detection of Colombian regulatory and preventive traffic signals. The system is tested in real environments on Colombian roads, obtaining an accuracy of over 90%. This research shows that computer vision-based methods are competitive against current proposals such as deep neural networks. |
| format | Online |
| id | oai:oai.revistas.uptc.edu.co:article-11760 |
| institution | Revista Facultad de Ingeniería |
| language | spa |
| publishDate | 2020 |
| publisher | Universidad Pedagógica y Tecnológica de Colombia |
| record_format | ojs |
| spelling | oai:oai.revistas.uptc.edu.co:article-117602021-07-13T02:23:02Z Driver-Assistant System Using Computer Vision and Machine Learning Sistema de asistencia a la conducción usando visión por computadora y aprendizaje máquina Valencia-Payan, Cristian Muñoz-Ordóñez, Julián Pencue-Fierro, Leonairo computer vision Haar classifier machine learning road safety traffic sign aprendizaje máquina clasificadores Haar seguridad vial señales de tránsito visión por computadora Safety has been one of the key points in vehicle design, in this case one of its main objectives is to implement warning systems to notify the driver about inappropriate or atypical process in their driving process, trying to avoid accidents that affect their vehicle passengers, as well as inflicting damage on third parties. Day by day, more systems are created to monitor the environment around the vehicle in order to ensure safe driving at all times. According to the World Health Organization, for 2016 there were 1.35 million deaths related to traffic accidents. This research presents the first driving assistance system developed for Colombia, the system detects and recognizes preventive and regulatory traffic signals and its precision is not affected by rotations and scale of the traffic signals present in an actual route, this is this way because the system is based on Haar classifiers. The system recognizes lane deviations, estimation of the curve direction and obstacle protruding along the way using computer vision algorithms, making it a low-cost computational system. Furthermore, this research provides the first resulting cascades for the detection of Colombian regulatory and preventive traffic signals. The system is tested in real environments on Colombian roads, obtaining an accuracy of over 90%. This research shows that computer vision-based methods are competitive against current proposals such as deep neural networks. La seguridad ha sido uno de los puntos claves en el diseño vehicular, por lo que uno de los principales objetivos es implementar sistemas de alerta para notificar al conductor sobre algún proceso inadecuado o atípico en su conducción, con el fin de evitar accidentes que afecten a sus ocupantes, así como a terceros; un ejemplo, de esto se observa en el auge de los vehículos autónomos. De acuerdo con la Organización Mundial de la Salud, en el 2016 se presentaron 1.35 millones de muertes relacionadas con accidentes de tráfico, por ello, actualmente se crean más sistemas para monitorizar el ambiente alrededor del vehículo de modo que se garantice una conducción segura en todo momento. Esta investigación presenta el primer sistema de asistencia a la conducción desarrollado para Colombia, el sistema detecta y reconoce señales de tránsito preventivas y reglamentarias basado en clasificadores Haar, lo cual permite que su precisión no se afecte debido a las rotaciones y escala de las señales presentes en un viaje sobre un trayecto real. El sistema reconoce salidas de carril, estimación de la dirección de la curva y detección de obstáculos que sobresalen en la carretera utilizando algoritmos de visión por computadora convirtiéndolo en un sistema de bajo costo computacional. Además, esta investigación proporciona los primeros clasificadores en cascada resultantes para la detección de señales reglamentarias y preventivas colombianas. El sistema es probado en ambientes reales de carreteras colombianas obteniendo una precisión superior al 90%. La investigación demuestra que métodos basados en visión por computadora son competitivos frente a propuestas actuales como las redes neuronales profundas. Universidad Pedagógica y Tecnológica de Colombia 2020-09-18 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf application/xml https://revistas.uptc.edu.co/index.php/ingenieria/article/view/11760 10.19053/01211129.v29.n54.2020.11760 Revista Facultad de Ingeniería; Vol. 29 No. 54 (2020): Continuos Publication; e11760 Revista Facultad de Ingeniería; Vol. 29 Núm. 54 (2020): Publicación Continua; e11760 2357-5328 0121-1129 spa https://revistas.uptc.edu.co/index.php/ingenieria/article/view/11760/9626 https://revistas.uptc.edu.co/index.php/ingenieria/article/view/11760/10014 Copyright (c) 2020 Cristian Valencia-Payan, M.Sc., Julián Muñoz-Ordóñez, M.Sc., Leonairo Pencue-Fierro |
| spellingShingle | computer vision Haar classifier machine learning road safety traffic sign aprendizaje máquina clasificadores Haar seguridad vial señales de tránsito visión por computadora Valencia-Payan, Cristian Muñoz-Ordóñez, Julián Pencue-Fierro, Leonairo Driver-Assistant System Using Computer Vision and Machine Learning |
| title | Driver-Assistant System Using Computer Vision and Machine Learning |
| title_alt | Sistema de asistencia a la conducción usando visión por computadora y aprendizaje máquina |
| title_full | Driver-Assistant System Using Computer Vision and Machine Learning |
| title_fullStr | Driver-Assistant System Using Computer Vision and Machine Learning |
| title_full_unstemmed | Driver-Assistant System Using Computer Vision and Machine Learning |
| title_short | Driver-Assistant System Using Computer Vision and Machine Learning |
| title_sort | driver assistant system using computer vision and machine learning |
| topic | computer vision Haar classifier machine learning road safety traffic sign aprendizaje máquina clasificadores Haar seguridad vial señales de tránsito visión por computadora |
| topic_facet | computer vision Haar classifier machine learning road safety traffic sign aprendizaje máquina clasificadores Haar seguridad vial señales de tránsito visión por computadora |
| url | https://revistas.uptc.edu.co/index.php/ingenieria/article/view/11760 |
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