A basis-background subtraction method using non-negative matrix factorization
Yaqi Chu, Xiaotian Wu, Tong Liu, Jun Liu · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2010
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.