In this paper, we proposed a basis-background subtraction method using non-negative matrix factorization
(NMF). The core idea is to learn the parts of complex background environments by NMF algorithm and exploit
the discrimination information in the training set to boost the reconstruction capability of the background
efficiently. The method utilize the distance between an observed image and the reconstructed background
image for segmenting foreground objects. The principle component analysis (PCA) is used for the enhanced
initialization of NMF algorithm. A kind of off-line basis-background maintenance scheme is introduced instead
of an incremental learning. A variety of experiments are conducted and illustrate the effectiveness in background
subtraction. Quantitative evaluation and comparison with the existing methods show that the proposed method
provides good improved results.
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