Multi-Channel Gaussian Derivative Neural Networks for Crowd Analysis

Hugo Gavilima-Pilataxi, Julio Ibarra-Fiallo

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

Resumen

This research shows the procedure to replace the image filtering for the counting of individuals carried out with a Gaussian filter kernel in order to obtain a density value (number of individuals) in a crowd, with Multi-Channel Gaussian Derivative Neural Networks. Gaussian operators, based in Scale-Space Theory, allows processing visual information in greater detail, especially in data sets for crowd counting with different scales, occlusion problems, or complex scenarios, which results in perfect candidates to be used as a primitive structure in a layer in deep neural network to significantly reduce the number of parameters in the model. Overall, the proposed mode achieves metrics comparable to high-level models, while using only approximately 10% of the parameters, which suggests a possible solution or future line of research for the study of urban congestion. In this way, Gaussian derivative neural network allows for more efficient processing of visual information and reduces the number of parameters required, making it an attractive option for crowd analysis in urban areas.

Idioma originalInglés
Título de la publicación alojada2023 IEEE 13th International Conference on Pattern Recognition Systems, ICPRS 2023
EditorialInstitute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)9798350333374
DOI
EstadoPublicada - 4 jul. 2023
Evento13th IEEE International Conference on Pattern Recognition Systems, ICPRS 2023 - Guayaquil, Ecuador
Duración: 4 jul. 20237 jul. 2023

Serie de la publicación

Nombre2023 IEEE 13th International Conference on Pattern Recognition Systems (ICPRS)

Conferencia

Conferencia13th IEEE International Conference on Pattern Recognition Systems, ICPRS 2023
País/TerritorioEcuador
CiudadGuayaquil
Período4/07/237/07/23

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