Robust person re-identification via graph convolution networks
DC Field | Value | Language |
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dc.contributor.author | Kim, Guisik | - |
dc.contributor.author | Shu, Dong Wook | - |
dc.contributor.author | Kwon, Junseok | - |
dc.date.accessioned | 2021-07-22T06:54:13Z | - |
dc.date.available | 2021-07-22T06:54:13Z | - |
dc.date.issued | 2021-08 | - |
dc.identifier.issn | 1380-7501 | - |
dc.identifier.issn | 1432-1882 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/47753 | - |
dc.description.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. | - |
dc.format.extent | 10 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | SPRINGER | - |
dc.title | Robust person re-identification via graph convolution networks | - |
dc.type | Article | - |
dc.identifier.doi | 10.1007/s11042-021-11127-0 | - |
dc.identifier.bibliographicCitation | MULTIMEDIA TOOLS AND APPLICATIONS, v.80, no.19, pp 29129 - 29138 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.wosid | 000662846600002 | - |
dc.identifier.scopusid | 2-s2.0-85108113131 | - |
dc.citation.endPage | 29138 | - |
dc.citation.number | 19 | - |
dc.citation.startPage | 29129 | - |
dc.citation.title | MULTIMEDIA TOOLS AND APPLICATIONS | - |
dc.citation.volume | 80 | - |
dc.type.docType | Article | - |
dc.publisher.location | 네델란드 | - |
dc.subject.keywordAuthor | Person re-identification | - |
dc.subject.keywordAuthor | Graph convolution | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Software Engineering | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Theory & Methods | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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