Cart (Loading....) | Create Account
Close category search window
 

Particle filter-based target tracking algorithm with adaptive multi-feature fusion

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

3 Author(s)
Mingming Wang ; Sch. of Inf. Sci. & Eng., Shandong Univ., Jinan, China ; Weining Zhang ; Yang Yang

Particle filter is a nonlinear filtering algorithm based on Bayesian estimation, which can well deal with non-linear, non Gaussian system parameter estimation and state filtering problem. In addition, as it allows integration of a variety of features, it is widely used in target tracking. This paper selects the best two features from color and shape-texture features according to their abilities of distinguishing the target from its background to describe the target. Taking the histograms of the two features as two target models, we can get two estimated results by using particle filter algorithm. If the two estimated results are similar, it means the selected features are effective and the result is reliable. On the contrary, it means that one or two of the selected features fail and target tracking fails. When tracking fails, the reliability of the estimated output of the previous frame is chosen to decide whether to return the previous frame to re select features and estimate in current frame again. Only when the two estimated results are similar to update the target model, thus can ensure the target model does not have a big offset. Experimental results show that the proposed algorithm is better than other particle filter algorithms using a single feature or two fixed features.

Published in:

Multimedia Technology (ICMT), 2011 International Conference on

Date of Conference:

26-28 July 2011

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.