Monocular Camera Fall Detection System Exploiting 3D Measures: A Semi-supervised Learning Approach

  • Konstantinos Makantasis
  • Eftychios Protopapadakis
  • Anastasios Doulamis
  • Lazaros Grammatikopoulos
  • Christos Stentoumis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7585)


Falls have been reported as the leading cause of injury-related visits to emergency departments and the primary etiology of accidental deaths in elderly. The system presented in this article addresses the fall detection problem through visual cues. The proposed methodology utilize a fast, real-time background subtraction algorithm based on motion information in the scene and capable to operate properly in dynamically changing visual conditions, in order to detect the foreground object and, at the same time, it exploits 3D space’s measures, through automatic camera calibration, to increase the robustness of fall detection algorithm which is based on semi-supervised learning. The above system uses a single monocular camera and is characterized by minimal computational cost and memory requirements that make it suitable for real-time large scale implementations.


image motion analysis semisupervised learning self calibration fall detection 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Konstantinos Makantasis
    • 1
  • Eftychios Protopapadakis
    • 1
  • Anastasios Doulamis
    • 1
  • Lazaros Grammatikopoulos
    • 2
  • Christos Stentoumis
    • 3
  1. 1.Technical University of CreteChaniaGreece
  2. 2.Technological Educational Institute of AthensAthensGreece
  3. 3.National Technical University of AthensAthensGreece

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