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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:

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.

Keywords

  • Learning Algorithm
  • Training Sample
  • Linear Representation
  • Background Subtraction
  • Sparse Representation

Publisher note

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Authors’ Affiliations

(1)
Department of Electrical Engineering, The Chinese University of Hong Kong, Hong Kong
(2)
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China

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