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.
This work is supported in part by European Commission 6th Framework Program with grant number FP6-507752 (MUSCLE Network of Excellence Project)
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Töreyin, B.U., Dedeoğlu, Y., Çetin, A.E. (2005). HMM Based Falling Person Detection Using Both Audio and Video. In: Sebe, N., Lew, M., Huang, T.S. (eds) Computer Vision in Human-Computer Interaction. HCI 2005. Lecture Notes in Computer Science, vol 3766. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11573425_21
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DOI: https://doi.org/10.1007/11573425_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29620-1
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