|
Abstract
|
Article Information:
RVM Based Human Fall Analysis for Video Surveillance Applications
B.Yogameena, G. Deepika and J. Mehjabeen
Corresponding Author: B. Yogameena
Submitted: March 18, 2012
Accepted: April 26, 2012
Published: December 15, 2012 |
Abstract:
|
For the safety of the elderly people, developed countries need to establish new healthcare systems
to ensure their safety at home. Computer vision and video surveillance provides a promising solution to analyze
personal behavior and detect certain unusual events such as falls. The main fall detection problem is to
recognize a fall among all the daily life activities, especially sitting down and crouching down activities which
have similar characteristics to falls (especially a large vertical velocity). In this study, a method is proposed to
detect falls by analyzing human shape deformation during a video sequence. In this study, Relevance Vector
Machine (RVM) is used to detect the fall of an individual based on the results obtained from torso angle
through skeletonization. Experimental results on benchmark datasets demonstrate that the proposed algorithm
is efficient. Further it is computationally inexpensive.
Key words: Fall detection, Gaussian Mixture Model (GMM) , Relevance Vector Machine (RVM), torso angle, video surveillance, ,
|
|
Abstract
|
PDF
|
HTML |
|
Cite this Reference:
B.Yogameena, G. Deepika and J. Mehjabeen, . RVM Based Human Fall Analysis for Video Surveillance Applications. Research Journal of Applied Sciences, Engineering and Technology, (24): 5361-5366.
|
|
|
|
 |
ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
 |
| Information |
|
|
|
|
| Sales & Services |
|
|
|
|
