Journal article Open Access

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

Manisha Talware; Sanjay Koli


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{
  "DOI": "10.35940/ijeat.C6524.029320", 
  "container_title": "International Journal of Engineering and Advanced Technology (IJEAT)", 
  "language": "eng", 
  "title": "Video-Based Person Re-Identification: Methods,  Datasets, and Deep Learning", 
  "issued": {
    "date-parts": [
      [
        2020, 
        2, 
        29
      ]
    ]
  }, 
  "abstract": "<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>", 
  "author": [
    {
      "family": "Manisha Talware"
    }, 
    {
      "family": "Sanjay Koli"
    }
  ], 
  "page": "4249-4254", 
  "volume": "9", 
  "type": "article-journal", 
  "issue": "3", 
  "id": "5595720"
}
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