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="" xmlns:oai_dc="" xmlns:xsi="" xsi:schemaLocation="">
  <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: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: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>Retrieval Number</dc:subject>
  <dc:title>Video-Based Person Re-Identification: Methods,  Datasets, and Deep Learning</dc:title>
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