Cotemporal Multi-View Video Segmentation - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Communication Dans Un Congrès Année : 2016

Cotemporal Multi-View Video Segmentation

Résumé

We address the problem of multi-view video segmentation of dynamic scenes in general and outdoor environments with possibly moving cameras. Multi-view methods for dynamic scenes usually rely on geometric calibration to impose spatial shape constraints between viewpoints. In this paper, we show that the calibration constraint can be relaxed while still getting competitive segmentation results using multi-view constraints. We introduce new multi-view cotemporality constraints through motion correlation cues, in addition to common appearance features used by co-segmentation methods to identify co-instances of objects. We also take advantage of learning based segmentation strategies by casting the problem as the selection of monocular proposals that satisfy multi-view constraints. This yields a fully automated method that can segment subjects of interest without any particular pre-processing stage. Results on several challenging outdoor datasets demonstrate the feasibility and robustness of our approach.
Fichier principal
Vignette du fichier
final_umvseg_3dv.pdf (1.83 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-01367430 , version 1 (16-09-2016)
hal-01367430 , version 2 (19-09-2016)

Identifiants

Citer

Abdelaziz Djelouah, Jean-Sébastien Franco, Edmond Boyer, Patrick Pérez, George Drettakis. Cotemporal Multi-View Video Segmentation. 3DV 2016 -International Conference on 3D Vision, Oct 2016, Stanford, United States. pp.360-369, ⟨10.1109/3DV.2016.45⟩. ⟨hal-01367430v2⟩
625 Consultations
512 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More