Detección 3D en infraestructuras inteligentes basada en imagen monocular
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En los últimos años, los avances en Deep Learning y Visión por Computador han impulsado el desarrollo de algoritmos de detección monocular aplicados a la gestión y seguridad del tráfico urbano, con el objetivo de optimizar la recolección de datos en entornos urbanos para las ciudades inteligentes del futuro. Sin embargo, estos esfuerzos han estado predominantemente enfocados en la extracción de datos desde la perspectiva del vehículo, pasando por alto las ventajas que ofrece el uso de cámaras instaladas en la infraestructura. Este artículo se centra en el estudio de la obtención de datos tridimensionales del tráfico desde esta perspectiva alternativa, aprovechando un punto de vista superior para evitar oclusiones y obtener información más precisa sobre el tamaño y la posición de los vehículos. Así, esta investigación propone un nuevo enfoque metodológico para la integración de sistemas de visión por computador basados en infraestructuras, aplicados a los Sistemas Inteligentes de Transporte.
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