Background Subtraction via Robust Dictionary Learning
Cong Zhao, Xiaogang Wang, Wai-Kuen Cham · EURASIP Journal on Image and Video Processing · 2011
We propose a learning-based background subtraction approach based on the theory of representation and dictionary learning. Our method makes the following two important assumptions: (1) the background of a scene has a 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.