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International Conference on Computer Vision and Graphics

ICCVG 2012: Computer Vision and Graphics pp 457–464Cite as

Human Fall Detection by Mean Shift Combined with Depth Connected Components

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 7594))

Abstract

Depth is very useful cue to achieve reliable fall detection since humans may not have consistent color and texture but must occupy an integrated region in space. In this work we demonstrate how to accomplish reliable fall detection using depth image sequences. The depth images are extracted by low-cost Kinect device. The person undergoing monitoring is extracted through mean-shift clustering. A depth connected component algorithm is used to delineate he/she in sequence of images. The system permits unobtrusive fall detection as well as preserves privacy of the user. The experimental results indicate high effectiveness of fall detection in indoor environments and low computational overhead of the algorithm.

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Author information

Authors and Affiliations

  1. Rzeszow University of Technology, Al. Powstańców Warszawy 12, 35-959, Rzeszów, Poland

    Michal Kepski & Bogdan Kwolek

Authors
  1. Michal Kepski
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  2. Bogdan Kwolek
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Editor information

Editors and Affiliations

  1. Polish-Japanese Institute of Information Technology, 86 Koszykowa, 02-008, Warsaw, Poland

    Leonard Bolc

  2. AGH University of Science and Technology, Mickiewicza 30, 30-059, Kraków, Poland

    Ryszard Tadeusiewicz

  3. Faculty of Applied Informatics and Mathematics, Department of Informatics, Warsaw University of Life Sciences (SGGW), Nowoursynowska 159, 02-776, Warsaw, Poland

    Leszek J. Chmielewski

  4. Polish-Japanese Institute of Information Technology, Koszykowa 86, 02-008, Warszawa, Poland

    Konrad Wojciechowski

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© 2012 Springer-Verlag Berlin Heidelberg

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Kepski, M., Kwolek, B. (2012). Human Fall Detection by Mean Shift Combined with Depth Connected Components. In: Bolc, L., Tadeusiewicz, R., Chmielewski, L.J., Wojciechowski, K. (eds) Computer Vision and Graphics. ICCVG 2012. Lecture Notes in Computer Science, vol 7594. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33564-8_55

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  • DOI: https://doi.org/10.1007/978-3-642-33564-8_55

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33563-1

  • Online ISBN: 978-3-642-33564-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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