Titre :

Analysis of Skeletal Shape Trajectories for Person Re-Identification

Auteur(s) :

Elaoud, Amani [Auteur]
Université de Tunis El Manar [UTM]
Barhoumi, Walid [Auteur]
Université de Carthage (Tunisie) [UCAR]
Drira, Hassen [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Zagrouba, Ezzeddine [Auteur]
Université de Tunis El Manar [UTM]

Éditeur(s) ou directeur(s) scientifique(s) :

Jacques Blanc-Talon
Rudi Penne
Wilfried Philips
Dan Popescu
Paul Scheunders

Titre de la manifestation scientifique :

ACIVS 2017 - 18th International Conference on Advanced Concepts for Intelligent Vision Systems

Ville :

Anvers

Pays :

Belgique

Date de début de la manifestation scientifique :

2017-09-18

Titre de l’ouvrage :

Lecture Notes in Computer Science

Titre de la revue :

Advanced Concepts for Intelligent Vision Systems 18th International Conference, ACIVS 2017

Éditeur :

Springer

Date de publication :

2017

Mot(s)-clé(s) en anglais :

Skeleton information
Similarity evaluation
Person re-identification
RGB-D sensors
Grassmann manifold

Discipline(s) HAL :

Informatique [cs]/Vision par ordinateur et reconnaissance de formes [cs.CV]

Résumé en anglais : [en]

In this paper, we are interested in people re-identification using skeleton information provided by a consumer RGB-D sensor. We perform the modelling and the analysis of human motion by focusing on 3D human joints given ...
Lire la suite >In this paper, we are interested in people re-identification using skeleton information provided by a consumer RGB-D sensor. We perform the modelling and the analysis of human motion by focusing on 3D human joints given by skeletons. In fact, the motion dynamic is modeled by projecting skeleton information on Grassmann manifold. Moreover, in order to define the identity of a test trajectory, we compare it against a labeled trajectory database while using an unsupervised similarity assessment procedure. Indeed, the main contribution of this work resides in the introduced distance that combines temporal information as well as global and local geometrical ones. Realized experiments on standard datasets prove that the proposed method performs accurately even though it does not assume any prior knowledge.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Collections :

• Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL