Journal article Open Access

Video-Based Person Re-Identification: Methods, Datasets, and Deep Learning

Manisha Talware; Sanjay Koli


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  <identifier identifierType="URL">https://zenodo.org/record/5595720</identifier>
  <creators>
    <creator>
      <creatorName>Manisha Talware</creatorName>
      <affiliation>Research Scholar at G.H. Raisoni College of  Engineering and Management, Pune, India</affiliation>
    </creator>
    <creator>
      <creatorName>Sanjay Koli</creatorName>
      <affiliation>Professor, D. Y. Patil Inst. of Info. Technology and  Research Supervisor at G.H. Raisoni College of Engineering and  Management, Pune, Indi</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Video-Based Person Re-Identification: Methods,  Datasets, and Deep Learning</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2020</publicationYear>
  <subjects>
    <subject>Person Re-Identification, Camera Network, Video Analytics, Deep Learning, pedestrian detection.</subject>
    <subject subjectScheme="issn">2249-8958</subject>
    <subject subjectScheme="handle">C6524029320 /2020©BEIESP</subject>
  </subjects>
  <contributors>
    <contributor contributorType="Sponsor">
      <contributorName>Blue Eyes Intelligence Engineering  &amp; Sciences Publication (BEIESP)</contributorName>
      <affiliation>Publisher</affiliation>
    </contributor>
  </contributors>
  <dates>
    <date dateType="Issued">2020-02-29</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="JournalArticle"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/5595720</alternateIdentifier>
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  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="ISSN" relationType="IsCitedBy" resourceTypeGeneral="JournalArticle">2249-8958</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.35940/ijeat.C6524.029320</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;Video Analytics applications like security and surveillance face a critical problem of person re-identification abbreviated as re-ID. The last decade witnessed the emergence of large-scale datasets and deep learning methods to use these huge data volumes. Most current re-ID methods are classified into either image-based or video-based re-ID. Matching persons across multiple camera views have attracted lots of recent research attention. Feature representation and metric learning are major issues for person re-identification. The focus of re-ID work is now shifting towards developing end-to-end re-Id and tracking systems for practical use with dynamic datasets. Most previous works contributed to the significant progress of person re-identification on still images using image retrieval models. This survey considers the more informative and challenging video-based person re-ID problem, pedestrian re-ID in particular. Publicly available datasets and codes are listed as a part of this work. Current trends which include open re-identification systems, use of discriminative features and deep learning is marching towards new applications in security and surveillance, typically for tracking.&lt;/p&gt;</description>
  </descriptions>
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