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Real-Time Fall Detection Method Based on Hidden Markov Modelling

  • Alban Meffre
  • Christophe Collet
  • Nicolas Lachiche
  • Pierre Gançarski
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7340)

Abstract

In the next few decades the increase of the number of elderly people will be of major concern, so that solutions must be found in order to maintain them at home. However such a population is exposed to the risk of falls, that can lead to dependency. This paper recalls some approaches used for fall detection and focuses on a method based on an uncalibrated camera. Motion detection uses a combination of simple Gaussian background modelling and interframe difference for person shape detection and features extraction. These features feed a Hidden Markov Model dedicated to fall detection. The algorithm has been tested on real data and we show that simple techniques can be used in order to obtain a fast and reliable fall detection system.

Keywords

Feature Extraction Motion Detection Hide State Video Processing Fall Detection 
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 2012

Authors and Affiliations

  • Alban Meffre
    • 1
  • Christophe Collet
    • 1
  • Nicolas Lachiche
    • 1
  • Pierre Gançarski
    • 1
  1. 1.LSIIT UMR 7005University of Strasbourg-CNRSIllkirch CedexFrance

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