Revisión de algoritmos de detección y seguimiento de objetos con redes profundas para videovigilancia inteligente

• Autores: David Ameijeiras Sánchez, Héctor Raúl González Diez, Yanio Hernández Heredia
• Localización: Revista Cubana de Ciencias Informáticas, ISSN-e 2227-1899, Vol. 14, Nº. 3, 2020
• Idioma: español
• Títulos paralelos:
  • Algorithms for detection and tracking objects with deep networks for intelligent video surveillance: A review
• Enlaces
  • Texto completo
• Dialnet Métricas: 4 Citas
• Resumen
  • español

    En la actualidad cada vez más se utilizan las redes neuronales profundas para resolver problemas de visión por computadora, como el reconocimiento y seguimiento de personas a través de una red de cámaras. Se realizó una revisión de los principales algoritmos de rastreo y detección de objetos, basados en redes profundas que permitirían conformar la arquitectura de un sistema de videovigilancia inteligente. Se determinó que: los algoritmos one-stage, son considerablemente más rápidos que los basados en propuestas de regiones, donde destaca SSD, y los algoritmos de rastreo offline tienen una mayor precisión comparado con los online, destacando a DeepSort como el más eficiente.

  • English

    Today, deep neural networks are increasingly used to solve computer vision problems, such as recognizing and tracking people through a network of cameras. A review of the main algorithms for tracking and object detection, based on deep networks, was carried out, which would make it possible to shape the architecture of an intelligent video surveillance system. It was determined that: one-stage algorithms are considerably faster than those based on region proposals, where SSD stands out, and offline tracking algorithms have a higher accuracy compared to online ones, highlighting DeepSort as the most efficient.

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