View in EDS HTML Full Text PDF Full Text

Joint representation learning of appearance and motion for abnormal event detection.

Saved in:
Bibliographic Details
Title: Joint representation learning of appearance and motion for abnormal event detection.
Authors: Yu, Jongmin1 jm.andrew.yu@gmail.com, Yow, Kin Choong1 kcyow@gist.ac.kr, Jeon, Moongu1 mgjeon@gist.ac.kr
Source: Machine Vision & Applications. Oct2018, Vol. 29 Issue 7, p1157-1170. 14p.
Subjects: Neural circuitry, UCSD Pascal (Computer program language), Robust control, Motion analysis, Artificial neural networks
Abstract: In this paper, we propose a joint learning of spatio-temporal representation based on 3D deep convolutional neural network for simultaneous representation of appearance and motion information in 3D volumes which are extracted from the multiple consecutive frames, and an end-to-end learning framework to detect abnormal events in surveillance scenes. By using the joint learning approach, the proposed framework can detect various abnormal events which can appear with diverse motion and appearance patterns. The proposed framework detects abnormal events in each volume by analyzing the spatio-temporal representation trained by the joint learning method. This volume-level event detection approach makes it possible to localize an abnormal event. We verify the proposed joint learning and the framework on the publicly available abnormal event datasets containing UMN dataset, UCSD dataset, and subway dataset, by comparing it with existing state-of-the-art methods. The experimental results demonstrate that the proposed joint learning and event detection method not only detect various abnormal events more efficiently but also localize anomalous regions more accurately. [ABSTRACT FROM AUTHOR]
Copyright of Machine Vision & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
Full text is not displayed to guests.
FullText Links:
  – Type: pdflink
Text:
  Availability: 1
Header DbId: egs
DbLabel: Engineering Source
An: 132044568
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Joint representation learning of appearance and motion for abnormal event detection.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Yu%2C+Jongmin%22">Yu, Jongmin</searchLink><relatesTo>1</relatesTo><i> jm.andrew.yu@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Yow%2C+Kin+Choong%22">Yow, Kin Choong</searchLink><relatesTo>1</relatesTo><i> kcyow@gist.ac.kr</i><br /><searchLink fieldCode="AR" term="%22Jeon%2C+Moongu%22">Jeon, Moongu</searchLink><relatesTo>1</relatesTo><i> mgjeon@gist.ac.kr</i>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Machine+Vision+%26+Applications%22">Machine Vision & Applications</searchLink>. Oct2018, Vol. 29 Issue 7, p1157-1170. 14p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Neural+circuitry%22">Neural circuitry</searchLink><br /><searchLink fieldCode="DE" term="%22UCSD+Pascal+%28Computer+program+language%29%22">UCSD Pascal (Computer program language)</searchLink><br /><searchLink fieldCode="DE" term="%22Robust+control%22">Robust control</searchLink><br /><searchLink fieldCode="DE" term="%22Motion+analysis%22">Motion analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: In this paper, we propose a joint learning of spatio-temporal representation based on 3D deep convolutional neural network for simultaneous representation of appearance and motion information in 3D volumes which are extracted from the multiple consecutive frames, and an end-to-end learning framework to detect abnormal events in surveillance scenes. By using the joint learning approach, the proposed framework can detect various abnormal events which can appear with diverse motion and appearance patterns. The proposed framework detects abnormal events in each volume by analyzing the spatio-temporal representation trained by the joint learning method. This volume-level event detection approach makes it possible to localize an abnormal event. We verify the proposed joint learning and the framework on the publicly available abnormal event datasets containing UMN dataset, UCSD dataset, and subway dataset, by comparing it with existing state-of-the-art methods. The experimental results demonstrate that the proposed joint learning and event detection method not only detect various abnormal events more efficiently but also localize anomalous regions more accurately. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Machine Vision & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=132044568
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1007/s00138-018-0961-8
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 14
        StartPage: 1157
    Subjects:
      – SubjectFull: Neural circuitry
        Type: general
      – SubjectFull: UCSD Pascal (Computer program language)
        Type: general
      – SubjectFull: Robust control
        Type: general
      – SubjectFull: Motion analysis
        Type: general
      – SubjectFull: Artificial neural networks
        Type: general
    Titles:
      – TitleFull: Joint representation learning of appearance and motion for abnormal event detection.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Yu, Jongmin
      – PersonEntity:
          Name:
            NameFull: Yow, Kin Choong
      – PersonEntity:
          Name:
            NameFull: Jeon, Moongu
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 10
              Text: Oct2018
              Type: published
              Y: 2018
          Identifiers:
            – Type: issn-print
              Value: 09328092
          Numbering:
            – Type: volume
              Value: 29
            – Type: issue
              Value: 7
          Titles:
            – TitleFull: Machine Vision & Applications
              Type: main
ResultId 1