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Communication Dans Un Congrès Année : 2018

Fusion of interest point/image based descriptors for efficient person re-identification

Résumé

The paper proposes a novel video-based person re-identification system that consists of describing a person using both Interest Points (IP) and Image-based features. The Image-based descriptor extracts global image representation that includes the silhouette but also possibly extra objects (i.e animal, stroller, etc) while the IP-based descriptor extracts salient points associated each with a local region of one of the objects. Two re-identification systems are proposed: an IP-based system using SURF interest points matched via sparse representation, and Image-based system using a Convolutional Neural Network. To harness both representations, we propose a fusing strategy based on the scores product rule, the scores being vote vectors associated with each descriptor for each person. Our proposal is evaluated on the large public dataset PRID-2011 and the results show its effectiveness compared to the state of the art
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Dates et versions

hal-01901844 , version 1 (23-10-2018)

Identifiants

Citer

Mohamed Ibn Khedher, Houda Jmila, Mounim El Yacoubi. Fusion of interest point/image based descriptors for efficient person re-identification. IJCNN 2018 : International Joint Conference on Neural Networks, Jul 2018, Rio De Janeiro, Brazil. pp.1 - 7, ⟨10.1109/IJCNN.2018.8489111⟩. ⟨hal-01901844⟩
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