• DocumentCode
    2866462
  • Title

    Fall detection with depth-videos

  • Author

    Aslan, Muzaffer ; Alcin, Omer Faruk ; Sengur, Abdulkadir ; Ince, Melih Cevdet

  • Author_Institution
    Gazi Endustri Meslek Lisesi, Elektron. Bolumu, Elazığ, Turkey
  • fYear
    2015
  • fDate
    16-19 May 2015
  • Firstpage
    443
  • Lastpage
    446
  • Abstract
    Elderly people, who are living alone are great risk if a fall event occurred. Therefore automatic fall detection systems are in demand for elderly people. In this paper, a depth based fall detection system is proposed. The proposed method consists of shape based fall characterization and a Support Vector Machine (SVM) classifier. Shape based fall characterization is formed with Curvature Scale Space (CSS) features and Fisher Vector (FV) encoding. According to the results obtained from experimental study, the proposed method has better performance than other methods that were published in literature.
  • Keywords
    computational geometry; geriatrics; image classification; object detection; support vector machines; vectors; CSS features; FV encoding; Fisher vector encoding; SVM classifier; automatic fall detection systems; curvature scale space features; depth-videos; elderly people; shape based fall characterization; support vector machine classifier; Conferences; Encoding; Feature extraction; Informatics; Senior citizens; Shape; Support vector machines; Curvature scale space; Fall decection; Fisher vector; Support vector machines;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Signal Processing and Communications Applications Conference (SIU), 2015 23th
  • Conference_Location
    Malatya
  • Type

    conf

  • DOI
    10.1109/SIU.2015.7129854
  • Filename
    7129854