Deep Learning Applied to Capacity Control in Commercial Establishments in Times of COVID-19

Ciro Rodriguez Rodriguez, Diana Luque, Carlos La Rosa, Doris Esenarro, Bishwajeet Pandey

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

1 Cita (Scopus)

Resumen

This research paper was developed to implement an intelligent solution for This research paper was developed to implement an intelligent solution for the control of the capacity of commercial establishments in times of COVID-19 using Yolo, which is a Convolutional Neural Network and a Deep Learning algorithm. For the application of this solution, a COCO dataset was used that is used in the implementation of Yolov4. A computer module was developed for the analysis of the flow of people, using Python 3.7, which mainly consists of an algorithm that determines the path and direction (movement) of a person, and this is evaluated in a limit o threshold that acts as the entrance and exit door of the main establishment; that is, it determines whether a person leaves or enters according to their route and direction. The results indicate that it is possible to implement this solution as an additional monitoring module for use as capacity control and with this offer a complete alternative to the owners of commercial establishments. In this way, it seeks to control the maximum capacity allowed due to the pandemic generated by the Sars-Cov.2 virus. The tests were conducted using an AMD Ryzen 7 3750H processor and an NVIDIA GTX 1660 TI video card. The possibility of determining whether the number of people who entered less than the number of people who left exceeds the maximum allowed by the pandemic on 50% of the real capacity.

Idioma originalInglés
Título de la publicación alojadaProceedings - 2020 12th International Conference on Computational Intelligence and Communication Networks, CICN 2020
EditoresGeetam Tomar
EditorialInstitute of Electrical and Electronics Engineers Inc.
Páginas423-428
Número de páginas6
ISBN (versión digital)9781728193939
DOI
EstadoPublicada - 25 sep 2020
Evento12th International Conference on Computational Intelligence and Communication Networks, CICN 2020 - Bhimtal, India
Duración: 25 sep 202026 sep 2020

Serie de la publicación

NombreProceedings - 2020 12th International Conference on Computational Intelligence and Communication Networks, CICN 2020

Conferencia

Conferencia12th International Conference on Computational Intelligence and Communication Networks, CICN 2020
País/TerritorioIndia
CiudadBhimtal
Período25/09/2026/09/20

Nota bibliográfica

Publisher Copyright:
© 2020 IEEE.

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