Background subtraction based on nonparametric Bayesian estimation

8 July 2011 Background subtraction based on nonparametric Bayesian estimation

Yan He, Donghui Wang, Miaoliang Zhu

Author Affiliations +

Proceedings Volume 8009, Third International Conference on Digital Image Processing (ICDIP 2011); 80090G (2011) https://doi.org/10.1117/12.896509
Event: 3rd International Conference on Digital Image Processing, 2011, Chengdu, China

Abstract

Background subtraction, the task of separating foreground pixels from background pixels in a video, is an important step in video processing. Comparing with the parametric background modeling methods, nonparametric methods use a model selection criterion to choose the right number of components for each pixel online. We model the background subtraction problem with the Dirichlet process mixture, which constantly adapts both the parameters and the number of components of the mixture to the scene.

Citation Download Citation

Yan He, Donghui Wang, and Miaoliang Zhu "Background subtraction based on nonparametric Bayesian estimation", Proc. SPIE 8009, Third International Conference on Digital Image Processing (ICDIP 2011), 80090G (8 July 2011); https://doi.org/10.1117/12.896509

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