• DocumentCode
    2714428
  • Title

    A unified framework for event summarization and rare event detection

  • Author

    Kwon, Junseok ; Lee, Kyoung Mu

  • Author_Institution
    Dept. of EECS, Seoul Nat. Univ., Seoul, South Korea
  • fYear
    2012
  • fDate
    16-21 June 2012
  • Firstpage
    1266
  • Lastpage
    1273
  • Abstract
    In this paper, we have proposed an unified framework for event summarization and rare event detection and presented the graph-structure learning and editing method to solve these problems efficiently. The experimental results demonstrated that the proposed method outperformed conventional algorithms in complex and crowded public scenes by exploiting and utilizing causality, frequency, and significance of relations of events.
  • Keywords
    graph theory; learning (artificial intelligence); video signal processing; complex public scenes; crowded public scenes; editing method; event summarization; graph-structure learning; rare event detection; unified framework; Data mining; Density functional theory; Event detection; Frequency measurement; Hidden Markov models; Markov processes; Proposals;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on
  • Conference_Location
    Providence, RI
  • ISSN
    1063-6919
  • Print_ISBN
    978-1-4673-1226-4
  • Electronic_ISBN
    1063-6919
  • Type

    conf

  • DOI
    10.1109/CVPR.2012.6247810
  • Filename
    6247810