DocumentCode :
3310569
Title :
A hybrid human fall detection scheme
Author :
Chen, Yie-Tarng ; Lin, Yu-Ching ; Fang, Wen-Hsien
Author_Institution :
Dept. of Electron. Eng., Nat. Taiwan Univ. of Sci. & Technol., Taipei, Taiwan
fYear :
2010
fDate :
26-29 Sept. 2010
Firstpage :
3485
Lastpage :
3488
Abstract :
This paper presents a novel video-based human fall detection system that can detect a human fall in real-time with a high detection rate. This fall detection system is based on an ingenious combination of skeleton feature and human shape variation, which can efficiently distinguish “fall-down” activities from “fall-like” ones. The experimental results indicate that the proposed human fall detection system can achieve a high detection rate and low false alarm rate.
Keywords :
image recognition; image thinning; shape recognition; video signal processing; high detection rate; human shape variation; hybrid human fall detection; skeleton feature; video-based human fall detection; Approximation methods; Feature extraction; Humans; Pixel; Real time systems; Shape; Skeleton; fall detection; human behavior analysis; skeleton;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Image Processing (ICIP), 2010 17th IEEE International Conference on
Conference_Location :
Hong Kong
ISSN :
1522-4880
Print_ISBN :
978-1-4244-7992-4
Electronic_ISBN :
1522-4880
Type :
conf
DOI :
10.1109/ICIP.2010.5650127
Filename :
5650127
Link To Document :
بازگشت