Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/26570
Title: Particle filter for targets tracking with motion model
Authors: Pang, GKH
Choy, KL 
Keywords: Target tracking
Kernel density estimation
Particle filter
Issue Date: 2013
Publisher: IEEE
Source: 2013 8th IEEE International Conference on Industrial and Information Systems (ICIIS), 17-20 December 2013, Peradeniya, p. 128-132 How to cite?
Abstract: Real-time robust tracking for multiple non-rigid objects is a challenging task in computer vision research. In recent years, stochastic sampling based particle filter has been widely used to describe the complicated target features of image sequence. In this paper, non-parametric density estimation and particle filter techniques are employed to model the background and track the object. Color feature and motion model of the target are extracted and used as key features in the tracking step, in order to adapt to multiple variations in the scene, such as background clutters, object's scale change and partial overlap of different targets. The paper also presents the experimental result on the robustness and effectiveness of the proposed method in a number of outdoor and indoor visual surveillance scenes.
URI: http://hdl.handle.net/10397/26570
ISBN: 978-1-4799-0908-7
DOI: 10.1109/ICIInfS.2013.6731968
Appears in Collections:Conference Paper

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

1
Citations as of Sep 16, 2017

Page view(s)

26
Last Week
0
Last month
Checked on Sep 17, 2017

Google ScholarTM

Check

Altmetric



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.