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Multi-shot SURF-based person re-identification via sparse representation

  • CNRS UMR 5157 SAMOVAR
  • Institut Pierre Simon Laplace, CNRS and CEA

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collectionContribution à une conférenceRevue par des pairs

25 Citations (Scopus)

Résumé

We present in this paper a multi-shot human reidentification system from video sequences based on SURF matching. Our contribution is about the matching step which is crucial. In this context, we propose a new method of SURF matching via sparse representation. Each SURF Interest Point in the test sequence is represented by a sparse representation of SURFs points in the reference dataset. For efficiency purposes, a dynamic dictionary is selected for each SURF from this dataset through KD-Tree Neighborhood search. Then a majority vote rule is applied to classify the test sequence. This approach is evaluated on two public datasets: PRID-2011 and CAVIAR4REID. The experimental results show that our approach compares favorably with and outperforms current state-of-the-art on the two datasets by 1% to 7%.

langue originaleAnglais
titre2013 10th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2013
EditeurIEEE Computer Society
Pages159-164
Nombre de pages6
ISBN (imprimé)9781479907038
Les DOIs
étatPublié - 1 janv. 2013
Modification externeOui
Evénement10th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2013 - Krakow, Pologne
Durée: 27 août 201330 août 2013

Série de publications

Nom2013 10th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2013

Une conférence

Une conférence10th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2013
Pays/TerritoirePologne
La villeKrakow
période27/08/1330/08/13

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