Un método para la detección y clasificación automática de accidentes de tráfico en un video mediante técnicas de aprendizaje profundo.
Según estadísticas a nivel mundial, los accidentes de tránsito son causantes de un porcentaje alto de muertes, llegando a ser, en algunos países, el segundo puesto en muertes mas violentas. El tráfico vehicular, el clima de la zona y el exceso de velocidad son algunos de los factores causantes de es...
- Autores:
-
Robles Serrano, Sergio Andres
- Tipo de recurso:
- Fecha de publicación:
- 2021
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/80540
- Palabra clave:
- 000 - Ciencias de la computación, información y obras generales::003 - Sistemas
Accidente de tránsito - Procesamiento de datos
Redes neuronales (computadores)
Neural networks (Computer science)
Traffic accidents
Urban traffic accident
Deep learning
Accident detection
Accident classification
Convolutional neural network
Recurrent neural network
Accidentes de tráfico
Aprendizaje profundo
- Rights
- openAccess
- License
- Atribución-NoComercial-SinDerivadas 4.0 Internacional
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| dc.title.spa.fl_str_mv |
Un método para la detección y clasificación automática de accidentes de tráfico en un video mediante técnicas de aprendizaje profundo. |
| dc.title.translated.eng.fl_str_mv |
A method for automatic detection and classification of traffic accidents in a video using deep learning techniques. |
| title |
Un método para la detección y clasificación automática de accidentes de tráfico en un video mediante técnicas de aprendizaje profundo. |
| spellingShingle |
Un método para la detección y clasificación automática de accidentes de tráfico en un video mediante técnicas de aprendizaje profundo. 000 - Ciencias de la computación, información y obras generales::003 - Sistemas Accidente de tránsito - Procesamiento de datos Redes neuronales (computadores) Neural networks (Computer science) Traffic accidents Urban traffic accident Deep learning Accident detection Accident classification Convolutional neural network Recurrent neural network Accidentes de tráfico Aprendizaje profundo |
| title_short |
Un método para la detección y clasificación automática de accidentes de tráfico en un video mediante técnicas de aprendizaje profundo. |
| title_full |
Un método para la detección y clasificación automática de accidentes de tráfico en un video mediante técnicas de aprendizaje profundo. |
| title_fullStr |
Un método para la detección y clasificación automática de accidentes de tráfico en un video mediante técnicas de aprendizaje profundo. |
| title_full_unstemmed |
Un método para la detección y clasificación automática de accidentes de tráfico en un video mediante técnicas de aprendizaje profundo. |
| title_sort |
Un método para la detección y clasificación automática de accidentes de tráfico en un video mediante técnicas de aprendizaje profundo. |
| dc.creator.fl_str_mv |
Robles Serrano, Sergio Andres |
| dc.contributor.advisor.none.fl_str_mv |
Sánchez Torres, German Branch Bedoya, John William |
| dc.contributor.author.none.fl_str_mv |
Robles Serrano, Sergio Andres |
| dc.contributor.researchgroup.spa.fl_str_mv |
GIDIA: Grupo de Investigación y Desarrollo en Inteligencia Artificial |
| dc.subject.ddc.spa.fl_str_mv |
000 - Ciencias de la computación, información y obras generales::003 - Sistemas |
| topic |
000 - Ciencias de la computación, información y obras generales::003 - Sistemas Accidente de tránsito - Procesamiento de datos Redes neuronales (computadores) Neural networks (Computer science) Traffic accidents Urban traffic accident Deep learning Accident detection Accident classification Convolutional neural network Recurrent neural network Accidentes de tráfico Aprendizaje profundo |
| dc.subject.lemb.spa.fl_str_mv |
Accidente de tránsito - Procesamiento de datos Redes neuronales (computadores) |
| dc.subject.lemb.eng.fl_str_mv |
Neural networks (Computer science) Traffic accidents |
| dc.subject.proposal.eng.fl_str_mv |
Urban traffic accident Deep learning Accident detection Accident classification Convolutional neural network Recurrent neural network |
| dc.subject.proposal.spa.fl_str_mv |
Accidentes de tráfico Aprendizaje profundo |
| description |
Según estadísticas a nivel mundial, los accidentes de tránsito son causantes de un porcentaje alto de muertes, llegando a ser, en algunos países, el segundo puesto en muertes mas violentas. El tráfico vehicular, el clima de la zona y el exceso de velocidad son algunos de los factores causantes de estos eventos. Por esto es cada vez mas importante la detección de este tipo de accidentes. Si bien ya existen diferentes alternativas para ayudar a la regulación de estos eventos, se necesita de un método automático que apoye este proceso. La duración del envío de una respuesta a una ocurrencia de un accidente de tráfico, se ve afectada en gran medida por el factor humano. Esto porque, en la ocurrencia de un evento de este tipo, la notificación del incidente debe ser dada por un humano, lo que limita el tiempo de respuesta prestado. El objetivo de este trabajo es establecer un método automático capaz de detectar y clasificar los accidentes de tráfico en video. Primero, se debe realizar una segmentación temporal del video de entrada. Luego se procesa por una red neuronal artificial con capas convolucionales y recurrentes para así detectar si el segmento presenta una escena de accidente. Por último, si se detectó con éxito el evento, se procesan los datos en otro modelo basado en redes neuronales artificiales capaz de clasificar el nivel de gravedad del accidente en las siguientes categorías: moderado y grave. Logrando una exactitud del 98% en la detección de accidentes en videos y un 81% en la clasificación según su nivel de gravedad. (Texto tomado de la fuente) |
| publishDate |
2021 |
| dc.date.accessioned.none.fl_str_mv |
2021-10-13T16:48:17Z |
| dc.date.available.none.fl_str_mv |
2021-10-13T16:48:17Z |
| dc.date.issued.none.fl_str_mv |
2021 |
| dc.type.spa.fl_str_mv |
Trabajo de grado - Maestría |
| dc.type.driver.spa.fl_str_mv |
info:eu-repo/semantics/masterThesis |
| dc.type.version.spa.fl_str_mv |
info:eu-repo/semantics/acceptedVersion |
| dc.type.content.spa.fl_str_mv |
Text |
| dc.type.redcol.spa.fl_str_mv |
http://purl.org/redcol/resource_type/TM |
| status_str |
acceptedVersion |
| dc.identifier.uri.none.fl_str_mv |
https://repositorio.unal.edu.co/handle/unal/80540 |
| dc.identifier.instname.spa.fl_str_mv |
Universidad Nacional de Colombia |
| dc.identifier.reponame.spa.fl_str_mv |
Repositorio Institucional Universidad Nacional de Colombia |
| dc.identifier.repourl.spa.fl_str_mv |
https://repositorio.unal.edu.co/ |
| url |
https://repositorio.unal.edu.co/handle/unal/80540 https://repositorio.unal.edu.co/ |
| identifier_str_mv |
Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia |
| dc.language.iso.spa.fl_str_mv |
spa |
| language |
spa |
| dc.relation.references.spa.fl_str_mv |
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Atribución-NoComercial-SinDerivadas 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Sánchez Torres, Germanee330f95e928bb5e6b06960faee68aa3Branch Bedoya, John William7e38ec86da58a9547c188086b39efee8600Robles Serrano, Sergio Andresf3ed61cb57759736387c8e367ad0251eGIDIA: Grupo de Investigación y Desarrollo en Inteligencia Artificial2021-10-13T16:48:17Z2021-10-13T16:48:17Z2021https://repositorio.unal.edu.co/handle/unal/80540Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/Según estadísticas a nivel mundial, los accidentes de tránsito son causantes de un porcentaje alto de muertes, llegando a ser, en algunos países, el segundo puesto en muertes mas violentas. El tráfico vehicular, el clima de la zona y el exceso de velocidad son algunos de los factores causantes de estos eventos. Por esto es cada vez mas importante la detección de este tipo de accidentes. Si bien ya existen diferentes alternativas para ayudar a la regulación de estos eventos, se necesita de un método automático que apoye este proceso. La duración del envío de una respuesta a una ocurrencia de un accidente de tráfico, se ve afectada en gran medida por el factor humano. Esto porque, en la ocurrencia de un evento de este tipo, la notificación del incidente debe ser dada por un humano, lo que limita el tiempo de respuesta prestado. El objetivo de este trabajo es establecer un método automático capaz de detectar y clasificar los accidentes de tráfico en video. Primero, se debe realizar una segmentación temporal del video de entrada. Luego se procesa por una red neuronal artificial con capas convolucionales y recurrentes para así detectar si el segmento presenta una escena de accidente. Por último, si se detectó con éxito el evento, se procesan los datos en otro modelo basado en redes neuronales artificiales capaz de clasificar el nivel de gravedad del accidente en las siguientes categorías: moderado y grave. Logrando una exactitud del 98% en la detección de accidentes en videos y un 81% en la clasificación según su nivel de gravedad. (Texto tomado de la fuente)According to worldwide statistics, traffic accidents are the cause of a high percentage of deaths, becoming, in some countries, the second most violent deaths. Vehicular traffic, the climate of the area and speed are some of the factors that cause these events. This is why it is increasingly important to detect these types of accidents. Although there are already different alternatives to help regulate these events, an automatic method is needed to support this process. The duration of sending a response to a traffic accident occurrence is largely affected by the human factor. This is because, in the occurrence of such an event, the notification of the incident must be given by a human, which limits the response time provided. The objective of this work is to establish an automatic method capable of detecting and classifying traffic accidents on video. First, a temporal segmentation of the input video must be performed. Then it is processed by an artificial neural network with convolutional and recurrent layers in order to detect if the segment presents an accident scene. Finally, if the event was successfully detected, the data is processed in another model based on artificial neural networks capable of classifying the level of severity of the accident in the following categories: moderate and severe. Achieving an accuracy of 98% in the detection of accidents in videos and 81% in the classification according to their level of severity. 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