• DocumentCode
    3310569
  • Title

    A hybrid human fall detection scheme

  • Author

    Chen, Yie-Tarng ; Lin, Yu-Ching ; Fang, Wen-Hsien

  • Author_Institution
    Dept. of Electron. Eng., Nat. Taiwan Univ. of Sci. & Technol., Taipei, Taiwan
  • fYear
    2010
  • fDate
    26-29 Sept. 2010
  • Firstpage
    3485
  • Lastpage
    3488
  • Abstract
    This paper presents a novel video-based human fall detection system that can detect a human fall in real-time with a high detection rate. This fall detection system is based on an ingenious combination of skeleton feature and human shape variation, which can efficiently distinguish “fall-down” activities from “fall-like” ones. The experimental results indicate that the proposed human fall detection system can achieve a high detection rate and low false alarm rate.
  • Keywords
    image recognition; image thinning; shape recognition; video signal processing; high detection rate; human shape variation; hybrid human fall detection; skeleton feature; video-based human fall detection; Approximation methods; Feature extraction; Humans; Pixel; Real time systems; Shape; Skeleton; fall detection; human behavior analysis; skeleton;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Image Processing (ICIP), 2010 17th IEEE International Conference on
  • Conference_Location
    Hong Kong
  • ISSN
    1522-4880
  • Print_ISBN
    978-1-4244-7992-4
  • Electronic_ISBN
    1522-4880
  • Type

    conf

  • DOI
    10.1109/ICIP.2010.5650127
  • Filename
    5650127