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

A novel supervised level set method for non-rigid object tracking

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)
Xin Sun ; Harbin Inst. of Technol., Harbin, China ; Hongxun Yao ; Shengping Zhang

We present a novel approach to non-rigid object tracking based on a supervised level set model (SLSM). In contrast with conventional level set models, which emphasize the intensity consistency only and consider no priors, the curve evolution of the proposed SLSM is object-oriented and supervised by the specific knowledge of the target we want to track. Therefore, the SLSM can ensure a more accurate convergence to the target in tracking applications. In particular, we firstly construct the appearance model for the target in an on-line boosting manner due to its strong discriminative power between objects and background. Then the probability of the contour is modeled by considering both the region and edge cues in a Bayesian manner, leading the curve converge to the candidate region with maximum likelihood of being the target. Finally, accurate target region qualifies the samples fed the boosting procedure as well as the target model prepared for the next time step. Positive decrease rate is used to adjust the learning pace over time, enabling tracking to continue under partial and total occlusion. Experimental results on a number of challenging sequences validate the effectiveness of the technique.

Published in:

Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on

Date of Conference:

20-25 June 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.