Improving open-set person re-identification by statistics-driven gallery refinement

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Person re-identification (re-ID) is a valuable tool for multi-camera tracking of persons. Up till now, research on person re-ID has mainly focused on the closed-set case, where a given query is assumed to always have a correct match in the gallery set, which does not hold for practical scenarios. In this study, we explore the open-set person re-ID problem with queries not always included in the gallery set. First, we convert the popular closed-set person re-ID datasets into the open-set scenario. Second, we compare the performances of six state-of-the-art closed-set person re-ID methods under open-set conditions. Third, we investigate the impact of a simple and fast statistics-driven gallery refinement approach on the open-set person re-ID performance. Extensive experimental evaluations show that, gallery refinement increases the performance of existing methods in the low false-accept rate (FAR) region, while simultaneously reducing the computational demands of retrieval. Results show an average detection and identification rate (DIR) increase of 7.91% and 3.31% on the DukeMTMC-reID and Market1501 datasets, respectively, for an FAR of 1%.

Original languageEnglish
Title of host publication12th International Conference on Machine Vision, ICMV 2019
EditorsWolfgang Osten, Dmitry Nikolaev, Jianhong Zhou
Number of pages8
ISBN (Electronic)9781510636439
Publication statusPublished - 31 Jan 2020
Event12th International Conference on Machine Vision, ICMV 2019 - Amsterdam, Netherlands
Duration: 16 Nov 201918 Nov 2019

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X


Conference12th International Conference on Machine Vision, ICMV 2019


  • Image retrieval
  • Open-set
  • Person re-identification


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