Article

HMM based falling person detection using both audio and video

Authors:

B. Uğur Töreyin,

Yiğithan Dedeoğlu,

A. Enis ÇetinAuthors Info & Claims

ICCV'05: Proceedings of the 2005 international conference on Computer Vision in Human-Computer Interaction

Pages 211 - 220

https://doi.org/10.1007/11573425_21

Published: 21 October 2005 Publication History

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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.

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Islam MNirjon S(2021)Sound-AdapterProceedings of the 20th International Conference on Information Processing in Sensor Networks (co-located with CPS-IoT Week 2021)10.1145/3412382.3458265(176-190)Online publication date: 18-May-2021
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Islam MNirjon SEskicioglu RMottola LPriyantha B(2019)SoundSemanticsProceedings of the 18th International Conference on Information Processing in Sensor Networks10.1145/3302506.3310402(217-228)Online publication date: 16-Apr-2019
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Anagnostopoulos TNtalianis KSkourlas CRamson SJohn KGrammati PStefanos GChristos D(2018)IoT-enabled fall verification of elderly and impaired people in smart citiesProceedings of the 22nd Pan-Hellenic Conference on Informatics10.1145/3291533.3291553(88-92)Online publication date: 29-Nov-2018
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ICCV'05: Proceedings of the 2005 international conference on Computer Vision in Human-Computer Interaction

October 2005

231 pages

ISBN:3540296204

Editors:
Nicu Sebe
Intelligent Systems Lab Amsterdam, University of Amsterdam, The Netherlands
,
Michael Lew
LIACS Media Lab, Leiden University
,
Thomas S. Huang
Beckman Institute, University of Illinois at Urbana-Champaign

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Springer-Verlag

Berlin, Heidelberg

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Published: 21 October 2005

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Islam MNirjon S(2021)Sound-AdapterProceedings of the 20th International Conference on Information Processing in Sensor Networks (co-located with CPS-IoT Week 2021)10.1145/3412382.3458265(176-190)Online publication date: 18-May-2021
https://dl.acm.org/doi/10.1145/3412382.3458265
Islam MNirjon SEskicioglu RMottola LPriyantha B(2019)SoundSemanticsProceedings of the 18th International Conference on Information Processing in Sensor Networks10.1145/3302506.3310402(217-228)Online publication date: 16-Apr-2019
https://dl.acm.org/doi/10.1145/3302506.3310402
Anagnostopoulos TNtalianis KSkourlas CRamson SJohn KGrammati PStefanos GChristos D(2018)IoT-enabled fall verification of elderly and impaired people in smart citiesProceedings of the 22nd Pan-Hellenic Conference on Informatics10.1145/3291533.3291553(88-92)Online publication date: 29-Nov-2018
https://dl.acm.org/doi/10.1145/3291533.3291553
Zhang YNeumann H(2018)An Empirical Study Towards Understanding How Deep Convolutional Nets Recognize FallsComputer Vision – ECCV 2018 Workshops10.1007/978-3-030-11024-6_8(112-127)Online publication date: 8-Sep-2018
https://dl.acm.org/doi/10.1007/978-3-030-11024-6_8
Yajai ARasmequan S(2017)Adaptive directional bounding box from RGB-D information for improving fall detectionJournal of Visual Communication and Image Representation10.1016/j.jvcir.2017.08.00849:C(257-273)Online publication date: 1-Nov-2017
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Madarshahian RCaicedo JArocha Zambrana D(2016)Benchmark problem for human activity identification using floor vibrationsExpert Systems with Applications: An International Journal10.1016/j.eswa.2016.06.02762:C(263-272)Online publication date: 15-Nov-2016
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Goudelis GTsatiris GKarpouzis KKollias SMakedon FMariottini GKorn OMaglogiannis IMetsis V(2015)Fall detection using history triple featuresProceedings of the 8th ACM International Conference on PErvasive Technologies Related to Assistive Environments10.1145/2769493.2769562(1-7)Online publication date: 1-Jul-2015
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Diaconita IReinhardt AChristin DRensing CYoussef MMascolo CKawsar F(2014)Bleep bleep!Proceedings of the 11th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services10.4108/icst.mobiquitous.2014.258035(110-119)Online publication date: 2-Dec-2014
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Iio TShiomi MKamei KSharma CHagita NKuzuoka HOno TImai MYoung J(2014)Social acceptance by elderly people of a fall-detection system with range sensors in a nursing homeProceedings of the second international conference on Human-agent interaction10.1145/2658861.2658910(153-156)Online publication date: 29-Oct-2014
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Liao YHuang CHsu S(2012)Slip and fall event detection using Bayesian Belief NetworkPattern Recognition10.1016/j.patcog.2011.04.01745:1(24-32)Online publication date: 1-Jan-2012
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