By Topic

Robust object tracking using mean shift and fast motion estimation

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
$33 $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)
Zhulin Li ; State Key Laboratory of Machine Perception, Peking University, Beijing, China ; Chao Xu ; Yan Li

Visual object tracking is still a challenging problem in computer vision. We use color-based mean shift (MS) tracking algorithm to track object. Meanwhile, we use Kalman to predict the initial location for MS tracker. Predictor can give a reasonable initial position that is close to the target location. This prediction may reduce mean shift iteration number. Then in order to make it more robust, we bring the fast motion estimation (FME) idea used in the video compression field into our system. It acts as a complementary technology to make the tracking result more robust when there exists large object displacement between two adjacent frames.

Published in:

Intelligent Signal Processing and Communication Systems, 2007. ISPACS 2007. International Symposium on

Date of Conference:

Nov. 28 2007-Dec. 1 2007