Robust mean shift tracking with improved background-weighted histogram

Liangwei Jiang; Rui Huang; Nong Sang

doi:10.1117/12.901523

2 December 2011 Robust mean shift tracking with improved background-weighted histogram

Liangwei Jiang, Rui Huang, Nong Sang

Proceedings Volume 8004, MIPPR 2011: Pattern Recognition and Computer Vision; 800409 (2011) https://doi.org/10.1117/12.901523
Event: Seventh International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2011), 2011, Guilin, China

Abstract

Tracking objects in videos using mean shift technique has brought to public attention. In this paper, we developed an improved tracking algorithm based on the mean shift framework. To represent the object model more accurately, the motion direction of the object which was estimated by the local motion filters was employed to weight the histogram. Besides, a wise object template updating strategy was proposed to adapt to the change of the object appearance caused by noise, deformation or occlusion. The experimental results on several real world scenarios shows that our approach has an excellent tracking performance comparing with the background weighted histogram mean shift tracking approach and traditional mean shift tracking method.

Citation Download Citation

Liangwei Jiang, Rui Huang, and Nong Sang "Robust mean shift tracking with improved background-weighted histogram", Proc. SPIE 8004, MIPPR 2011: Pattern Recognition and Computer Vision, 800409 (2 December 2011); https://doi.org/10.1117/12.901523

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