DocumentCode :
2798126
Title :
A robust fall detection system for the elderly in a Smart Room
Author :
Yu, Miao ; Naqvi, Syed Mohsen ; Chambers, Jonathon
Author_Institution :
Electron. & Electr. Eng. Dept., Loughborough Univ., Leicester, UK
fYear :
2010
fDate :
14-19 March 2010
Firstpage :
1666
Lastpage :
1669
Abstract :
In this paper, we propose a novel and robust fall detection system by using a density method for modeling a fall event as a function of certain video feature.3-D head velocity and human shape information are extracted as feature and three types of density model, single Gaussian, mixture of Gaussians and Parzen window method, are constructed for modeling the density of fall with respect to the extracted video feature. Falls are then detected according to the corresponding obtained density model and the success of the method is confirmed on real video sequences.
Keywords :
Gaussian processes; feature extraction; handicapped aids; shape recognition; 3-D head velocity; Parzen window method; density method; fall event; feature extraction; human shape information; robust fall detection system; single Gaussian; smart room; video feature; Cameras; Data mining; Feature extraction; Filtering; Head; Humans; Particle tracking; Robustness; Senior citizens; Shape; code-book background subtraction; density method; fall detection; head tracking; motion-based particle filtering;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics Speech and Signal Processing (ICASSP), 2010 IEEE International Conference on
Conference_Location :
Dallas, TX
ISSN :
1520-6149
Print_ISBN :
978-1-4244-4295-9
Electronic_ISBN :
1520-6149
Type :
conf
DOI :
10.1109/ICASSP.2010.5495512
Filename :
5495512
Link To Document :
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