Introducing FoxPersonTracks: A benchmark ...
Type de document :
Autre communication scientifique (congrès sans actes - poster - séminaire...): Communication dans un congrès avec actes
Titre :
Introducing FoxPersonTracks: A benchmark for person re-identification from TV broadcast shows
Auteur(s) :
Auguste, Rémi [Auteur]
FOX MIIRE [LIFL]
Tirilly, Pierre [Auteur]
FOX MIIRE [LIFL]
Martinet, Jean [Auteur]
FOX MIIRE [LIFL]
FOX MIIRE [LIFL]
Tirilly, Pierre [Auteur]
FOX MIIRE [LIFL]
Martinet, Jean [Auteur]
FOX MIIRE [LIFL]
Titre de la manifestation scientifique :
International Workshop on Content-Based Multimedia Indexing
Ville :
Prague
Pays :
République tchèque
Date de début de la manifestation scientifique :
2015-06-10
Date de publication :
2015
Discipline(s) HAL :
Informatique [cs]/Vision par ordinateur et reconnaissance de formes [cs.CV]
Informatique [cs]/Multimédia [cs.MM]
Informatique [cs]/Multimédia [cs.MM]
Résumé en anglais : [en]
This paper introduces a novel person track dataset dedicated to person re-identification. The dataset is built from a set of real life TV shows broadcasted from BFMTV and LCP TV french channels, provided during REPERE ...
Lire la suite >This paper introduces a novel person track dataset dedicated to person re-identification. The dataset is built from a set of real life TV shows broadcasted from BFMTV and LCP TV french channels, provided during REPERE challenge. It contains a total 4,604 persontracks (short video sequences featuring an individual with no background) from 266 persons. The dataset has been built from the REPERE dataset by following several automated processing and manual selection/filtering steps. It is meant to serve as a benchmark in person re-identification from images/videos. The dataset also provides re-identifications results using space-time histograms as a baseline, together with an evaluation tool in order to ease the comparison to other re-identification methods.Lire moins >
Lire la suite >This paper introduces a novel person track dataset dedicated to person re-identification. The dataset is built from a set of real life TV shows broadcasted from BFMTV and LCP TV french channels, provided during REPERE challenge. It contains a total 4,604 persontracks (short video sequences featuring an individual with no background) from 266 persons. The dataset has been built from the REPERE dataset by following several automated processing and manual selection/filtering steps. It is meant to serve as a benchmark in person re-identification from images/videos. The dataset also provides re-identifications results using space-time histograms as a baseline, together with an evaluation tool in order to ease the comparison to other re-identification methods.Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Internationale
Vulgarisation :
Non
Collections :
Source :
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