December 30, 2019 Journal article Open Access

Neha Sharma; Pradeep Kumar D,; Rohit Kumar; Shiv Dutt Tripathi

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  <identifier identifierType="URL">https://zenodo.org/record/5599225</identifier>
  <creators>
    <creator>
      <creatorName>Neha Sharma</creatorName>
      <affiliation>Computer Science and Engineering, Ramaiah Institute of  Technology, Bangalore, India.</affiliation>
    </creator>
    <creator>
      <creatorName>Pradeep Kumar D,</creatorName>
      <familyName>Pradeep Kumar D</familyName>
      <affiliation>Computer Science and Engineering, Ramaiah Institute of  Technology, Bangalore, India.</affiliation>
    </creator>
    <creator>
      <creatorName>Rohit Kumar</creatorName>
      <affiliation>Computer Science and Engineering, Ramaiah Institute of  Technology, Bangalore, India.</affiliation>
    </creator>
    <creator>
      <creatorName>Shiv Dutt Tripathi</creatorName>
      <affiliation>Computer Science and Engineering, Ramaiah Institute of  Technology, Bangalore, India.</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Anomaly Detection in Human Behavior using  Video Surveillance</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2019</publicationYear>
  <subjects>
    <subject>video surveillance, anomaly detection, semi-supervised learning, unusual activity, video processing, abnormal behavior.</subject>
    <subject subjectScheme="issn">2249-8958</subject>
    <subject subjectScheme="handle">B3133129219/2019©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">2019-12-30</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="JournalArticle"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/5599225</alternateIdentifier>
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    <relatedIdentifier relatedIdentifierType="ISSN" relationType="IsCitedBy" resourceTypeGeneral="JournalArticle">2249-8958</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.35940/ijeat.B3133.129219</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;Conventional static surveillance has proved to be quite ineffective as the huge number of cameras to keep an eye on most often outstrips the monitor&amp;rsquo;s ability to do so. Furthermore, the amount of focus needed to constantly monitor the surveillance video cameras is often overbearing. The review paper focuses on solving the problem of anomaly detection in video sequence through semi-supervised techniques. Each video is defined as sequence of frames. The model is trained with goal to minimize the reconstruction error which later on is used to detect anomaly in the test sample videos. The model was trained and tested on most commonly used benchmarking dataset&amp;nbsp; Avenue dataset. Experiment results confirm that the model detects anomaly in a video with a reasonably good accuracy in presence of some noise in dataset.&lt;/p&gt;</description>
  </descriptions>
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