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                <term xml:lang="fr">Suivi animaux</term>
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              <p>Cet article aborde le problème de la ré-identification (ReID) de chiens à partir de pièges photographiques qui fournissent des vidéos de mauvaise qualité et des petits ensembles de données. Notre objectif est de définir un extracteur de caractéristiques (EC) robuste à ces conditions difficiles. Nous revisitons l'optimisation de la fonction de coût par triplet par la définition d'exemples difficiles lors de trois apprentissages consécutifs. Nous avons mené des expériences sur deux ensembles de données que nous avons rendus publics, l'ensemble de données YT-BB-Dog et SEAdogSEA. Les résultats de précision top-k dans le scénario de ReID sur l'ensemble de données YT-BB-Dog étiqueté sont bons, compte tenu des conditions difficiles. Nous proposons une chaîne de traitement complète utilisant une détection et un suivi standard et une méthode de ReID fondée sur notre EC robuste. Pour finir, nous donnons une évaluation qualitative sur l'ensemble de données SEAdogSEA.</p>
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