Conteo de vehículos a partir de vídeos usando machine learning
| dc.contributor.advisor | Pedraza Bonilla, Cesar Augusto | spa |
| dc.contributor.author | Navarro Ávila, Juan Sebastián | spa |
| dc.date.accessioned | 2020-05-12T01:53:08Z | spa |
| dc.date.available | 2020-05-12T01:53:08Z | spa |
| dc.date.issued | 2020-02-13 | spa |
| dc.description.abstract | This work presents a framework for vehicle counting from videos, using deep neural networks as detectors. The framework has 4 stages: preprocessing, detection and classification, tracking, and post-processing. For the detection stage, several deep object detector are compared and 3 new ones are proposed based on Tiny YOLOv3. For the tracking, a new tracker based on IOU is compared against the classic ones: Boosting, KCF, TLD, Mediaflow, MOSSE and CSRT. The comparison is based on 8 multi-object tracking metrics over the Bog19 dataset. The Bog19 dataset is a collection of annotated videos from the city of Bogota. The annotations include bicycles, buses, cars, motorbikes and trucks. Finally, the system is evaluated for the task of vehicle counting on this dataset. For the counting task, the combinations of the proposed detectors with the Medianflow and MOSSE trackers obtain the best results. The founded detectors have the same performance as those of the state of the art but with a higher speed. | spa |
| dc.description.abstract | Este trabajo presenta un framework para el conteo de vehı́culos a partir de videos, utilizando redes neuronales profundas como detectores. El framework tiene 4 etapas: preprocesamiento, detección y clasificación, seguimiento y post-procesamiento. Para la etapa de detección se comparan varios detectores de objetos profundos y se proponen 3 nuevos basados en Tiny YOLOv3. Para el rastreo, se compara un nuevo rastreador basado en IOU con los clásicos: Boosting, KCF, TLD, Mediaflow, MOSSE y CSRT. La comparación se hace en base a 8 métricas de seguimiento multiobjeto sobre el conjunto de datos del Bog19. El conjunto de datos Bog19 es una colección de videos anotados de la ciudad de Bogotá. Las clases de objetos anotados incluyen bicicletas, autobuses, coches, motos y camiones. Finalmente el sistema es evaluado para la tarea de contar vehı́culos en este conjunto de datos. Para la tarea de conteo, las combinaciones de los detectores propuestos y los rastreadores Medianflow y MOSSE obtienen los mejores resultados. Los detectores encontrados tienen el mismo desempeño que los del estado del arte pero con una mayor velocidad. | spa |
| dc.description.additional | Magíster en Ingeniería - Ingeniería de Sistemas y Computación | spa |
| dc.description.degreelevel | Maestría | spa |
| dc.format.extent | 60 | spa |
| dc.format.mimetype | application/pdf | spa |
| dc.identifier.uri | https://repositorio.unal.edu.co/handle/unal/77510 | |
| dc.language.iso | eng | spa |
| dc.publisher.branch | Universidad Nacional de Colombia - Sede Bogotá | spa |
| dc.publisher.program | Bogotá - Ingeniería - Maestría en Ingeniería - Ingeniería de Sistemas y Computación | spa |
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| dc.rights | Derechos reservados - Universidad Nacional de Colombia | spa |
| dc.rights.accessrights | info:eu-repo/semantics/openAccess | spa |
| dc.rights.license | Atribución-SinDerivadas 4.0 Internacional | spa |
| dc.rights.spa | Acceso abierto | spa |
| dc.rights.uri | http://creativecommons.org/licenses/by-nd/4.0/ | spa |
| dc.subject.ddc | 620 - Ingeniería y operaciones afines | spa |
| dc.subject.proposal | Vehicle | eng |
| dc.subject.proposal | Vehı́culo | spa |
| dc.subject.proposal | Video analysis | eng |
| dc.subject.proposal | Análisis de video | spa |
| dc.subject.proposal | Aprendizaje de maquina | spa |
| dc.subject.proposal | Machine learning | eng |
| dc.subject.proposal | Computer vision | eng |
| dc.subject.proposal | Visión por computador | spa |
| dc.subject.proposal | Aprendizaje profundo | spa |
| dc.subject.proposal | Deep learning | eng |
| dc.subject.proposal | Object detection | eng |
| dc.subject.proposal | Detección de objectos | spa |
| dc.subject.proposal | Rastreo de objetos | spa |
| dc.subject.proposal | Object tracking | eng |
| dc.title | Conteo de vehículos a partir de vídeos usando machine learning | spa |
| dc.type | Trabajo de grado - Maestría | spa |
| dc.type.coar | http://purl.org/coar/resource_type/c_bdcc | spa |
| dc.type.coarversion | http://purl.org/coar/version/c_ab4af688f83e57aa | spa |
| dc.type.content | Text | spa |
| dc.type.driver | info:eu-repo/semantics/masterThesis | spa |
| dc.type.version | info:eu-repo/semantics/acceptedVersion | spa |
| oaire.accessrights | http://purl.org/coar/access_right/c_abf2 | spa |