• DocumentCode
    596649
  • Title

    Local spatio-temporal interest point detection for human action recognition

  • Author

    Feng Li ; Jixiang Du

  • Author_Institution
    Dept. of Comput. Sci. & Technol., Huaqiao Univ., Xiamen, China
  • fYear
    2012
  • fDate
    18-20 Oct. 2012
  • Firstpage
    579
  • Lastpage
    582
  • Abstract
    This paper presents a unified action recognition framework combining harris3D descriptor with 3D SIFT detector. We perform action recognition experiments on the KTH dataset using Support Vector Machines. Experiments apply the leave-one-out and compare our proposed approach with state-of-the-art methods. The result shows that our proposed approach is effective. Compared with other approaches our approach is more robust, easier to compute.
  • Keywords
    feature extraction; object recognition; spatiotemporal phenomena; support vector machines; 3D SIFT detector; KTH dataset; harris3D descriptor; human action recognition; spatiotemporal interest point detection; support vector machine; Computer vision; Computers; Conferences; Detectors; Educational institutions; Feature extraction; Humans;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Advanced Computational Intelligence (ICACI), 2012 IEEE Fifth International Conference on
  • Conference_Location
    Nanjing
  • Print_ISBN
    978-1-4673-1743-6
  • Type

    conf

  • DOI
    10.1109/ICACI.2012.6463231
  • Filename
    6463231