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Robust person re-identification via graph convolution networks

Authors
Kim, GuisikShu, Dong WookKwon, Junseok
Issue Date
Aug-2021
Publisher
SPRINGER
Keywords
Person re-identification; Graph convolution
Citation
MULTIMEDIA TOOLS AND APPLICATIONS, v.80, no.19, pp 29129 - 29138
Pages
10
Journal Title
MULTIMEDIA TOOLS AND APPLICATIONS
Volume
80
Number
19
Start Page
29129
End Page
29138
URI
https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/47753
DOI
10.1007/s11042-021-11127-0
ISSN
1380-7501
1432-1882
Abstract
Person re-identification (re-id) aims to identity the same person over multiple cameras; it has been successfully applied to various computer vision applications as a fundamental method. Owing to the development of deep learning, person re-id methods, which typically use triplet networks based on triplet loss, have demonstrated great success. However, the appearances of people are similar and hence difficult to distinguish in many cases. Therefore, we present a novel graph convolution network and enhances traditional triplet loss functions. Our method defines reference, positive, and negative features for triplet loss as three vertices of a graph, respectively, and adjusts their mutual distance through learning. The method adopts graph convolutions efficiently, thereby affording low computational costs. Experimental results demonstrate that our method is superior to the baseline on the Market-1501 dataset. The proposed GCN-based triplet loss considerably contributes to improve re-identification methods quantitatively and qualitatively.
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소프트웨어대학 (소프트웨어학부)
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