HMM based falling person detection using both audio and video

Authors:
B. Uğur Töreyin;Yiğithan Dedeoğlu;A. Enis Çetin
Affiliations:
Department of Electrical and Electronics Engineering, Bilkent University, Bilkent, Ankara, Turkey;Department of Computer Engineering, Bilkent University, Bilkent, Ankara, Turkey;Department of Electrical and Electronics Engineering, Bilkent University, Bilkent, Ankara, Turkey
Venue:
ICCV'05 Proceedings of the 2005 international conference on Computer Vision in Human-Computer Interaction
Year:
2005

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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.