Open Access

Background Subtraction via Robust Dictionary Learning

EURASIP Journal on Image and Video Processing20112011:972961

https://doi.org/10.1155/2011/972961

Received: 14 May 2010

Accepted: 18 January 2011

Published: 9 February 2011

Abstract

We propose a learning-based background subtraction approach based on the theory of sparse representation and dictionary learning. Our method makes the following two important assumptions: (1) the background of a scene has a sparse linear representation over a learned dictionary; (2) the foreground is "sparse" in the sense that majority pixels of the frame belong to the background. These two assumptions enable our method to handle both sudden and gradual background changes better than existing methods. As discussed in the paper, the way of learning the dictionary is critical to the success of background modeling in our method. To build a correct background model when training samples are not foreground-free, we propose a novel robust dictionary learning algorithm. It automatically prunes foreground pixels out as outliers at the learning stage. Experiments in both qualitative and quantitative comparisons with competing methods demonstrate the obtained robustness against background changes and better performance in foreground segmentation.

Publisher note

To access the full article, please see PDF.

Authors’ Affiliations

(1)
Department of Electrical Engineering, The Chinese University of Hong Kong
(2)
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences

Copyright

© Cong Zhao et al. 2011

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.