By Topic

Visual tracking based on multiple instance learning particle filter

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)
Yu Song ; Sch. of Electron. & Inf. Eng., Beijing Jiaotong Univ., Beijing, China ; Qingling Li

The tracking by detection algorithms treat visual tracking as the on-line object and its local surround background classification problem. The main shortcoming of the algorithms is the template drift due to the online self-learning mechanism of the visual tracker. To overcome the problem, a novel online Multiple Instance Learning (MIL) particle filter visual tracking algorithm is proposed. Main contributions of our work are: Firstly, we introduce online MIL Boosting algorithm in particle filter visual tracking framework to deal with the problem of target appearance model online learning by noisy labeled samples and to evaluate the importance weight for each particle; Secondly, the particle set, which represents the probability distribution density of the tracked target state, is utilized to construct the online training positive bag for the MIL Boosting classifier; At last, some experimental results show the proposed algorithm is a robust and accuracy tracking algorithm.

Published in:

Mechatronics and Automation (ICMA), 2011 International Conference on

Date of Conference:

7-10 Aug. 2011