DOI: 10.3724/SP.J.1249.2016.05511

Journal of Shenzhen University Science and Engineering (深圳大学学报理工版) 2016/33:5 PP.511-516

Adaptive background modeling via incremental non-negative matrix factorization

A method for adaptive background modeling based on the incremental non-negative matrix factorization (INMF) is proposed. INMF is used to update new background models effectively when new data streams arrive. The experimental results show that, compared with non-negative matrix factorization (NMF), INMF not only takes less running time but also can be used to extract better foregrounds.

Key words:applied mathematics,non-negative matrix factorization,background modeling,incremental learning,feature extraction,full rank factorization,foreground extraction

ReleaseDate:2016-09-27 14:21:24

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