Person Re-identification with a Body Orientation-Specific Convolutional Neural Network - Laboratoire d’Excellence Intelligences des Mondes Urbains
Communication Dans Un Congrès Année : 2018

Person Re-identification with a Body Orientation-Specific Convolutional Neural Network

Andrei Stoian
  • Fonction : Auteur
  • PersonId : 964723
Jean-Yves Dufour
  • Fonction : Auteur

Résumé

Person re-identification consists in matching images of a particular person captured in a network of cameras with non-overlapping fields of view. The challenges in this task arise from the large variations of human appearance. In particular, the same person could show very different appearances from different points of view. To address this challenge, in this paper we propose an Orientation-Specific Convolutional Neural Network (OSCNN) framework which jointly performs body orientation regression and extracts orientation-specific deep representations for person re-identification. A robust joint embedding is obtained by combining feature representations under different body orientations. We experimentally show on two public benchmarks that taking into account body orientations improves the person re-identification performance. Moreover , our approach outperforms most of the previous state-of-the-art re-identification methods on these benchmarks.
Fichier principal
Vignette du fichier
ACIVS_OSCNN.pdf (1.54 Mo) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01895374 , version 1 (15-10-2018)

Identifiants

  • HAL Id : hal-01895374 , version 1

Citer

Yiqiang Chen, Stefan Duffner, Andrei Stoian, Jean-Yves Dufour, Atilla Baskurt. Person Re-identification with a Body Orientation-Specific Convolutional Neural Network. Advanced Concepts for Intelligent Vision systems, Sep 2018, Poitiers, France. ⟨hal-01895374⟩
325 Consultations
508 Téléchargements

Partager

More