A Multilevel Strategy to Improve People Tracking in a Real-World Scenario

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Détails bibliographiques
Auteurs principaux:	de Oliveira, Cristiano B., Neves, Joao C., Ribeiro, Rafael O., Menotti, David
Format:	Preprint
Publié:	2024
Sujets:	Computer Vision and Pattern Recognition
Accès en ligne:	Acceder al recurso
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author	de Oliveira, Cristiano B. Neves, Joao C. Ribeiro, Rafael O. Menotti, David
author_facet	de Oliveira, Cristiano B. Neves, Joao C. Ribeiro, Rafael O. Menotti, David
contents	The Palácio do Planalto, office of the President of Brazil, was invaded by protesters on January 8, 2023. Surveillance videos taken from inside the building were subsequently released by the Brazilian Supreme Court for public scrutiny. We used segments of such footage to create the UFPR-Planalto801 dataset for people tracking and re-identification in a real-world scenario. This dataset consists of more than 500,000 images. This paper presents a tracking approach targeting this dataset. The method proposed in this paper relies on the use of known state-of-the-art trackers combined in a multilevel hierarchy to correct the ID association over the trajectories. We evaluated our method using IDF1, MOTA, MOTP and HOTA metrics. The results show improvements for every tracker used in the experiments, with IDF1 score increasing by a margin up to 9.5%.
format	Preprint
id	arxiv_https___arxiv_org_abs_2404_18876
institution	arXiv
publishDate	2024
record_format	arxiv
spellingShingle	A Multilevel Strategy to Improve People Tracking in a Real-World Scenario de Oliveira, Cristiano B. Neves, Joao C. Ribeiro, Rafael O. Menotti, David Computer Vision and Pattern Recognition The Palácio do Planalto, office of the President of Brazil, was invaded by protesters on January 8, 2023. Surveillance videos taken from inside the building were subsequently released by the Brazilian Supreme Court for public scrutiny. We used segments of such footage to create the UFPR-Planalto801 dataset for people tracking and re-identification in a real-world scenario. This dataset consists of more than 500,000 images. This paper presents a tracking approach targeting this dataset. The method proposed in this paper relies on the use of known state-of-the-art trackers combined in a multilevel hierarchy to correct the ID association over the trajectories. We evaluated our method using IDF1, MOTA, MOTP and HOTA metrics. The results show improvements for every tracker used in the experiments, with IDF1 score increasing by a margin up to 9.5%.
title	A Multilevel Strategy to Improve People Tracking in a Real-World Scenario
topic	Computer Vision and Pattern Recognition
url	https://arxiv.org/abs/2404.18876

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