Journal article Open Access

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

Manisha Talware; Sanjay Koli


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<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:contributor>Blue Eyes Intelligence Engineering  &amp; Sciences Publication (BEIESP)</dc:contributor>
  <dc:creator>Manisha Talware</dc:creator>
  <dc:creator>Sanjay Koli</dc:creator>
  <dc:date>2020-02-29</dc:date>
  <dc:description>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.</dc:description>
  <dc:identifier>https://zenodo.org/record/5595720</dc:identifier>
  <dc:identifier>10.35940/ijeat.C6524.029320</dc:identifier>
  <dc:identifier>oai:zenodo.org:5595720</dc:identifier>
  <dc:language>eng</dc:language>
  <dc:relation>issn:2249-8958</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
  <dc:source>International Journal of Engineering and Advanced Technology (IJEAT) 9(3) 4249-4254</dc:source>
  <dc:subject>Person Re-Identification, Camera Network, Video Analytics, Deep Learning, pedestrian detection.</dc:subject>
  <dc:subject>ISSN</dc:subject>
  <dc:subject>Retrieval Number</dc:subject>
  <dc:title>Video-Based Person Re-Identification: Methods,  Datasets, and Deep Learning</dc:title>
  <dc:type>info:eu-repo/semantics/article</dc:type>
  <dc:type>publication-article</dc:type>
</oai_dc:dc>
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