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Introducing a Statistical Behavior Model into Camera-Based Fall Detection

  • Andreas Zweng
  • Sebastian Zambanini
  • Martin Kampel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6453)

Abstract

Camera based fall detection represents a solution to the problem of people falling down and being not able to stand up on their own again. For elderly people who live alone, such a fall is a major risk. In this paper we present an approach for fall detection based on multiple cameras supported by a statistical behavior model. The model describes the spatio-temporal unexpectedness of objects in a scene and is used to verify a fall detected by a semantic driven fall detection. In our work a fall is detected using multiple cameras where each of the camera inputs results in a separate fall confidence. These confidences are then combined into an overall decision and verified with the help of the statistical behavior model. This paper describes the fall detection approach as well as the verification step and shows results on 73 video sequences.

Keywords

Motion Speed Multiple Camera Foreground Pixel Fall Detection Fall Event 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Andreas Zweng
    • 1
  • Sebastian Zambanini
    • 1
  • Martin Kampel
    • 1
  1. 1.Computer Vision LabVienna University of TechnologyViennaAustria

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