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Asian Conference on Computer Vision

ACCV 2012: Computer Vision - ACCV 2012 Workshops pp 121–132Cite as

Robust Fall Detection by Combining 3D Data and Fuzzy Logic

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 7729))

Abstract

Falls are a major risk for the elderly and where immediate help is needed. The elderly, especially when suffering from dementia, are not able to react to emergency situations properly, thus falls need to be detected automatically. An overview of different classes of fall detection approaches is presented and a vision-based approach is introduced. We propose the use of a Kinect to obtain 3D data in combination with fuzzy logic for robust fall detection and show that our approach outperforms current state-of-the-art algorithms. Our approach is evaluated on 72 video sequences, containing 40 falls and 32 activities of daily living.

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Author information

Authors and Affiliations

  1. Computer Vision Lab, Vienna University of Technology, Favoritenstr. 9-11/183-2, A-1040, Vienna, Austria

    Rainer Planinc & Martin Kampel

Authors
  1. Rainer Planinc
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  2. Martin Kampel
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Editor information

Editors and Affiliations

  1. Computer Science and Engineering, Hanyang University, 222 Wangshimni-ro, Seongdong-gu, 133-791, Seoul, South Korea

    Jong-Il Park

  2. Department of Electrical Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, 305-701, Daejeon, South Korea

    Junmo Kim

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Planinc, R., Kampel, M. (2013). Robust Fall Detection by Combining 3D Data and Fuzzy Logic. In: Park, JI., Kim, J. (eds) Computer Vision - ACCV 2012 Workshops. ACCV 2012. Lecture Notes in Computer Science, vol 7729. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37484-5_11

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  • DOI: https://doi.org/10.1007/978-3-642-37484-5_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37483-8

  • Online ISBN: 978-3-642-37484-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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