Journal article Open Access

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

Manisha Talware; Sanjay Koli


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  "inLanguage": {
    "alternateName": "eng", 
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    "name": "English"
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  "about": [
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      "@type": "CreativeWork"
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      "@id": "https://hdl.handle.net/C6524029320 /2020\u00a9BEIESP", 
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  "description": "<p>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.</p>", 
  "license": "https://creativecommons.org/licenses/by/4.0/legalcode", 
  "creator": [
    {
      "affiliation": "Research Scholar at G.H. Raisoni College of  Engineering and Management, Pune, India", 
      "@type": "Person", 
      "name": "Manisha Talware"
    }, 
    {
      "affiliation": "Professor, D. Y. Patil Inst. of Info. Technology and  Research Supervisor at G.H. Raisoni College of Engineering and  Management, Pune, Indi", 
      "@type": "Person", 
      "name": "Sanjay Koli"
    }
  ], 
  "headline": "Video-Based Person Re-Identification: Methods,  Datasets, and Deep Learning", 
  "image": "https://zenodo.org/static/img/logos/zenodo-gradient-round.svg", 
  "datePublished": "2020-02-29", 
  "keywords": [
    "Person Re-Identification, Camera Network, Video Analytics, Deep Learning, pedestrian detection."
  ], 
  "url": "https://zenodo.org/record/5595720", 
  "contributor": [
    {
      "affiliation": "Publisher", 
      "@type": "Person", 
      "name": "Blue Eyes Intelligence Engineering  & Sciences Publication (BEIESP)"
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  "identifier": "https://doi.org/10.35940/ijeat.C6524.029320", 
  "@id": "https://doi.org/10.35940/ijeat.C6524.029320", 
  "@type": "ScholarlyArticle", 
  "name": "Video-Based Person Re-Identification: Methods,  Datasets, and Deep Learning"
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