research-article

Video-surveillance and context aware system for activity recognition

Authors:

Xanthi Kolovou,

Ilias MaglogiannisAuthors Info & Claims

PETRA '10: Proceedings of the 3rd International Conference on PErvasive Technologies Related to Assistive Environments

Article No.: 64, Pages 1 - 2

https://doi.org/10.1145/1839294.1839371

Published: 23 June 2010 Publication History

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Abstract

Fall detection is the main issue in design an AAL system for elderly. This paper describes an algorithm for vision and audio fall detection. The main problem with video surveillance is the distinction of a fall from similar daily activities such as lying down, kneeling, standing up, walking or falling. The goal of this research is to design a reliable fall detection system which not only relies on video analysis, but also uses the information from environment of the patient to create context information.

References

[1]

U. Laessoe, H. C. Hoeck, O. Simonsen et al., 'Fall risk in an active elderly population can it be assessed?', Journal of Negative Results in BioMedicine, 6:2, Jan, 2007.

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[2]

Xinquo Yu, Xiao Wang, Panachit Kittipanya-Ngam, How Lung Eng, Loong-Fah Cheong 2009. Fall Detection and Alert for Ageing-at-Home of elderly, ICOST 2009, LNCS 5597, pp. 209--216 2009.

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[3]

Jared Willems, Glen Debard, Bert Bonroy, Bart Vanrumste, Toon Goedeme, 2009. How to detect human fall in video? An overview. Positioning and Context-Awareness International Conference, POCA 2009, Antwerp, Belgium (May 2009).

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Ugur Toreyin, Yigithan Dedeoglou, Enis Cetin: HMM Based Falling Person Detection, N. Sebe, M. S. Lew, and T. S. Huang (Eds.): HCI/ICCV 2005, LNCS 3766, pp. 211--220, 2005. Springer-Verlag Berlin Heidelberg.

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[5]

Charalampos Doukas, Ilias Maglogiannis "Enabling" Human Status Awareness in Assistive Environments based on Advanced Sound and Motion Data Classification", presented at The 1st ACM International Conference on PErvasive Technologies Related to Assistive Environments (PETRAE), July 16--19 2008, Athens, Greece.

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Index Terms

Video-surveillance and context aware system for activity recognition
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Object recognition
        Reconstruction
        Shape inference
        Tracking
      2. Computer vision tasks
        Scene understanding
        Video summarization
  2. Computer graphics
    1. Shape modeling

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Information

Published In

PETRA '10: Proceedings of the 3rd International Conference on PErvasive Technologies Related to Assistive Environments

June 2010

452 pages

ISBN:9781450300711

DOI:10.1145/1839294

Editors:
Fillia Makedon
University of Texas at Arlington
,
Ilias Maglogiannis
University of Central Greece, Greece
,
Sarantos Kapidakis
Ionian University, Greece

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 June 2010

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PETRA '10

PETRA '10: The 3rd International Conference on PErvasive Technologies Related to Assistive Environments

June 23 - 25, 2010

Samos, Greece

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