Vehicle and pedestrian video-tracking with classification based on deep convolutional neural networks

Alejandro Forero, Francisco Calderon

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

12 Citas (Scopus)

Resumen

In this article we propose an algorithm for the classification, tracking and counting of vehicles and pedestrians in video sequences; The algorithm is divided into two parts, a classification algorithm, which is based on convolutional neural networks, implemented using the You Only Look Once (YOLO) method; and a proposed algorithm for tracking regions of interest based in a well defined taxonomy. For the first stage of classification, We train and evaluate the performance with a set of more than 50000 labels, which we make available for their use. The tracking algorithm is evaluated against manual counts in video sequences of different scenarios captured in the management center of the Secretaria distrital de Movilidad of Bogota.

Idioma originalInglés
Título de la publicación alojada2019 22nd Symposium on Image, Signal Processing and Artificial Vision, STSIVA 2019 - Conference Proceedings
EditorialInstitute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)9781728114910
DOI
EstadoPublicada - abr. 2019
Evento22nd Symposium on Image, Signal Processing and Artificial Vision, STSIVA 2019 - Bucaramanga, Colombia
Duración: 24 abr. 201926 abr. 2019

Serie de la publicación

Nombre2019 22nd Symposium on Image, Signal Processing and Artificial Vision, STSIVA 2019 - Conference Proceedings

Conferencia

Conferencia22nd Symposium on Image, Signal Processing and Artificial Vision, STSIVA 2019
País/TerritorioColombia
CiudadBucaramanga
Período24/04/1926/04/19

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