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Communication Dans Un Congrès Année : 2018

Person Re-identification using group context

Andrei Stoian
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  • PersonId : 964723
Jean-Yves Dufour
  • Fonction : Auteur

Résumé

The person re-identification task consists in matching person images detected from surveillance cameras with non-overlapping fields of view. Most existing approaches are based on the person's visual appearance. However, one of the main challenges, especially for a large gallery set, is that many people wear very similar clothing. Our proposed approach addresses this issue by exploiting information on the group of persons around the given individual. In this way, possible ambiguities are reduced and the discriminative power for person re-identification is enhanced , since people often walk in groups and even tend to walk alongside strangers. In this paper, we propose to use a deep convolutional neural networks (CNN) to extract group feature representations that are invariant to the relative displacements of individuals within a group. Then we use this group feature representation to perform group association under non-overlapping cameras. Furthermore, we propose a neural network framework to combine the group cue with the single person feature representation to improve the person re-identification performance. We experimentally show that our deep group feature representation achieves a better group association performance than the state-of-the-art methods and that taking into account group context improves the accuracy of the individual re-identification.
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Dates et versions

hal-01895373 , version 1 (15-10-2018)

Identifiants

  • HAL Id : hal-01895373 , version 1

Citer

Yiqiang Chen, Stefan Duffner, Andrei Stoian, Jean-Yves Dufour, Atilla Baskurt. Person Re-identification using group context. Advanced Concepts for Intelligent Vision systems, Sep 2018, Poitiers, France. ⟨hal-01895373⟩
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