Visual tracking with multifeature joint sparse representation

Wenhui Dong; Faliang Chang; Zijian Zhao

doi:10.1117/1.JEI.24.1.013006

7 January 2015 Visual tracking with multifeature joint sparse representation

Wenhui Dong, Faliang Chang, Zijian Zhao

Author Affiliations +

Journal of Electronic Imaging, Vol. 24, Issue 1, 013006 (January 2015). https://doi.org/10.1117/1.JEI.24.1.013006

Abstract

We present a visual tracking method with feature fusion via joint sparse presentation. The proposed method describes each target candidate by combining different features and joint sparse representation for robustness in coefficient estimation. Then, we build a probabilistic observation model based on the approximation error between the recovered candidate image and the observed sample. Finally, this observation model is integrated with a stochastic affine motion model to form a particle filter framework for visual tracking. Furthermore, a dynamic and robust template update strategy is applied to adapt the appearance variations of the target and reduce the possibility of drifting. Quantitative evaluations on challenging benchmark video sequences demonstrate that the proposed method is effective and can perform favorably compared to several state-of-the-art methods.

Citation Download Citation

Wenhui Dong, Faliang Chang, and Zijian Zhao "Visual tracking with multifeature joint sparse representation," Journal of Electronic Imaging 24(1), 013006 (7 January 2015). https://doi.org/10.1117/1.JEI.24.1.013006

Published: 7 January 2015

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available