Modelo de un sistema de alerta basado en visión artificial para la prevención de aglomeraciones en el transporte público

Lucia Cárdenas; Andrea Pipino; María Teresa Donadio; Paulo Picota

doi:10.48204/j.vian.v8n2.a6571

Submitted December 16, 2024

Published 2024-12-16

Artículos

Vol. 8 No. 2 (2024): Visión Antataura

Model of a machine vision-based alert system for crowd prevention in public transportation

Lucia Cárdenas⁺⁻
Andrea Pipino⁺⁻
María Teresa Donadio⁺⁻
Paulo Picota⁺⁻

DOI https://doi.org/10.48204/j.vian.v8n2.a6571

PDF (Español (España))

References

DOI: 10.48204/j.vian.v8n2.a6571

Published: 2024-12-16

How to Cite

Cárdenas, L., Pipino, A., Donadio, M. T., & Picota, P. (2024). Model of a machine vision-based alert system for crowd prevention in public transportation. Visión Antataura, 8(2), 93–107. https://doi.org/10.48204/j.vian.v8n2.a6571

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Abstract

Artificial intelligence has become an integral part. Of the many applications and services that we use on a daily basis. This paper presents the design of a model that uses artificial vision technologies to detect and identify the concentration of people and generate alerts according to the values. Provided by a specific classification algorithm for crowds. The project is focused on serving the Panamanian public. Considering that currently transportation companies do not have tools that allow the management of data to obtain information about the conditions. Of the passengers along the different routes. The main objective is to develop a model that can detect agglomerations in real time and provide information to transport operators about the number of users on the buses, and as a result, improve the decision-making process. Libraries applied to artificial vision are used in order to propose an efficient and fast solution, which also allows continuous improvement, facilitating management. Prevention of overcrowding. As a result, the proposed system detected 84.04% of crowds, demonstrating the possibility of optimizing passenger flow.

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References

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