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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Bibliographic Details
Published in:IEEE transactions on circuits and systems for video technology Vol. 21; no. 5; pp. 611 - 622
Main Authors: Rougier, Caroline, Meunier, Jean, St-Arnaud, Alain, Rousseau, Jacqueline
Format: Journal Article
Language:English
Published: New York, NY IEEE 01.05.2011
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
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
Online Access:Get full text
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Description
Summary: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 method is proposed to detect falls by analyzing human shape deformation during a video sequence. A shape matching technique is used to track the person's silhouette along the video sequence. The shape deformation is then quantified from these silhouettes based on shape analysis methods. Finally, falls are detected from normal activities using a Gaussian mixture model. This paper has been conducted on a realistic data set of daily activities and simulated falls, and gives very good results (as low as 0% error with a multi-camera setup) compared with other common image processing methods.
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ISSN:1051-8215
1558-2205
DOI:10.1109/TCSVT.2011.2129370