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HMM Based Falling Person Detection Using Both Audio and Video

  • B. Uğur Töreyin
  • Yiğithan Dedeoğlu
  • A. Enis Çetin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3766)

Abstract

Automatic detection of a falling person in video is an important problem with applications in security and safety areas including supportive home environments and CCTV surveillance systems. Human motion in video is modeled using Hidden Markov Models (HMM) in this paper. In addition, the audio track of the video is also used to distinguish a person simply sitting on a floor from a person stumbling and falling. Most video recording systems have the capability of recording audio as well and the impact sound of a falling person is also available as an additional clue. Audio channel data based decision is also reached using HMMs and fused with results of HMMs modeling the video data to reach a final decision.

Keywords

Hide Markov Model Wavelet Coefficient Audio Signal Wavelet Domain 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 2005

Authors and Affiliations

  • B. Uğur Töreyin
    • 1
  • Yiğithan Dedeoğlu
    • 2
  • A. Enis Çetin
    • 1
  1. 1.Department of Electrical and Electronics EngineeringBilkent UniversityBilkent, AnkaraTurkey
  2. 2.Department of Computer EngineeringBilkent UniversityBilkent, AnkaraTurkey

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