By Topic

On the use of joint estimation in particle filters for object tracking in video

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Kuchi, P. ; Center for Cognitive Ubiquitous Comput., Arizona State Univ., Tempe, AZ, USA ; Panchanathan, S.

Object tracking is an important problem, whose effective solution is crucial to lot of applications. Though several methods have been proposed in the literature, they fail when the object of interest does not conform to a specific process model. Also, current methods have to be tuned for different videos and objects (say, for example, using training data). One solution for such a problem is to estimate the parameters of the process model while estimating the state. In this paper, we propose such joint estimation of particle filters for object tracking and show that for the same model for state estimation, particle filtering with joint estimation performs better (in terms of the tracking error) than conventional particle filtering.

Published in:

TENCON 2004. 2004 IEEE Region 10 Conference  (Volume:A )

Date of Conference:

21-24 Nov. 2004