Improving vehicle re‐identification using CNN latent spaces: Metrics comparison and track‐to‐track extension

Improving vehicle re‐identification using CNN latent spaces: Metrics comparison and track‐to‐track extension

Abstract Herein, the problem of vehicle re‐identification using distance comparison of images in CNN latent spaces is addressed. First, the impact of the distance metrics, comparing performances obtained with different metrics is studied: the minimal Euclidean distance (MED), the minimal cosine dist...

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Bibliographic Details
Main Authors: Geoffrey Roman‐Jimenez, Patrice Guyot, Thierry Malon, Sylvie Chambon, Vincent Charvillat, Alain Crouzil, André Péninou, Julien Pinquier, Florence Sedes, Christine Sénac
Format: Article
Language:English
Published: Wiley 2021-03-01
Series:IET Computer Vision
Online Access:https://doi.org/10.1049/cvi2.12010

Internet

https://doi.org/10.1049/cvi2.12010

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