DUNE: Deep UNcertainty Estimation for tracked visual features - Équipe Robotique et InteractionS Accéder directement au contenu
Communication Dans Un Congrès Année : 2022

DUNE: Deep UNcertainty Estimation for tracked visual features

Résumé

Uncertainty estimation of visual feature is essential for vision-based systems, such as visual navigation. We show that errors inherent to visual tracking, in particular using KLT tracker, can be learned using a probabilistic loss function to estimate the covariance matrix on each tracked feature position. The proposed system is trained and evaluated on synthetic data, as well as on real data, highlighting good results in comparison to the state of the art. The benefits of the tracking uncertainty estimates are illustrated for visual motion estimation.
Fichier principal
Vignette du fichier
IPAS_final_paper.pdf (6.63 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03790281 , version 1 (05-11-2022)

Identifiants

Citer

Katia Katia Lillo, Andrea de Maio, Simon Lacroix, Amaury Nègre, Michèle Rombaut, et al.. DUNE: Deep UNcertainty Estimation for tracked visual features. IPAS 2022 - 5th IEEE International Conference on Image Processing, Applications and Systems (IPAS 2022), Dec 2022, Genova, Italy. ⟨10.1109/PRDC55274.2022.00021⟩. ⟨hal-03790281⟩
119 Consultations
40 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More