Automated tool to collect vehicle trajectories using drone images and computer vision techniques

Authors

  • Alessandro Macêdo de Araújo Universidade Federal do Ceará, Fortaleza, Ceará, Brasil https://orcid.org/0000-0002-5933-6065
  • Thiago Passos Oliveira Universidade Federal do Ceará, Fortaleza, Ceará, Brasil
  • Manoel Mendonça de Castro Neto Universidade Federal do Ceará, Fortaleza, Ceará, Brasil https://orcid.org/0000-0002-6317-4863
  • Diêgo Farias de Oliveira Universidade Federal do Ceará, Fortaleza, Ceará, Brasil
  • João Paulo Pordeus Gomes Universidade Federal do Ceará, Fortaleza, Ceará, Brasil

DOI:

https://doi.org/10.58922/transportes.v31i3.2886

Keywords:

Urban traffic, Urban streets, YOLO, Deep SORT

Abstract

The main objective of this work is to propose a procedure for automated collection of vehicle trajectories using a computer vision tool, applied to videos recorded by drone. The algorithm was trained to automatically detect, classify, and track vehicles, and the data were processed to obtain the trajectories in 4 sites in Fortaleza. The test results indicate a good performance to detect and classify, mainly cars, motorcycles, and trucks (98% to 99% true positive rate). It was verified the importance of correcting the position of objects to compensate for the drone movements caused by winds. The vehicle passage times and the headways obtained were similar to those recorded using a semiautomatic tool, with 98.6% of time differences between 0.0 and 0.2 s and 95.3% of headway differences between -0.1 and +0.1 s.

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Published

2023-12-11

How to Cite

Macêdo de Araújo, A., Passos Oliveira, T., Mendonça de Castro Neto, M., Farias de Oliveira, D., & Pordeus Gomes, J. P. (2023). Automated tool to collect vehicle trajectories using drone images and computer vision techniques. TRANSPORTES, 31(3), e2886. https://doi.org/10.58922/transportes.v31i3.2886

Issue

Vol. 31 No. 3 (2023)

Section

Artigos Vencedores do Prêmio ANPET Produção Científica

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Copyright (c) 2023 Alessandro Macêdo de Araújo, Thiago Passos Oliveira, Manoel Mendonça de Castro Neto, Diêgo Farias de Oliveira, João Paulo Pordeus Gomes

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