Robust Video Surveillance for Fall Detection Based on Human Shape Deformation

Faced with the growing population of seniors, developed countries need to establish new healthcare systems to ensure the safety of elderly people at home. Computer vision provides a promising solution to analyze personal behavior and detect certain unusual events such as falls. In this paper, a new...

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Vydáno v:	IEEE transactions on circuits and systems for video technology Ročník 21; číslo 5; s. 611 - 622
Hlavní autoři:	Rougier, Caroline, Meunier, Jean, St-Arnaud, Alain, Rousseau, Jacqueline
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York, NY IEEE 01.05.2011 Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Applied sciences Artificial intelligence Cameras Circuits Computer science; control theory; systems Computer vision Deformation Exact sciences and technology Fall detection Gaussian Gaussian mixture model (GMM) Health care Human Humans Image edge detection Image processing Information, signal and communications theory Machine vision Mathematical models novelty detection Older people Pattern recognition Pattern recognition. Digital image processing. Computational geometry procrustes shape analysis Services and terminals of telecommunications Shape shape context Signal processing Studies Surveillance Systems, networks and services of telecommunications Telecommunications Telecommunications and information theory Telemetry. Remote supervision. Telewarning. Remote control Three dimensional displays Video sequences video surveillance Deformation Image processing Video signal Mixture theory Fall detection Remote sensing Multi-view technology Image sequence Morphological analysis Safety shape context Pattern analysis video surveillance Novelty detection Human Computer vision Target tracking Video technique Pattern recognition Image method Gaussian process Procrustes shape analysis Gaussian mixture model (GMM) Elderly Pattern matching Remote supervision
ISSN:	1051-8215, 1558-2205
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