TY - GEN
T1 - Incremental non-negative matrix factorization for dynamic background modelling
AU - Bucak, Serhat S.
AU - Gunsel, Bilge
AU - Gursoy, Ozan
PY - 2007
Y1 - 2007
N2 - In this paper, an incremental algorithm which is derived from Non-negative Matrix Factorization (NMF) is proposed for background modeling in surveillance type of video sequences. The adopted algorithm, which is called as Incremental NMF (INMF), is capable of modeling dynamic content of the surveillance video and controlling contribution of the subsequent observations to the existing representation properly. INMF preserves additive, parts-based representation, and dimension reduction capability of NMF without increasing the computational load. Test results are reported to compare background modeling performances of batch-mode and incremental NMF in surveillance type of video. Moreover, test results obtained by the incremental PCA are also given for comparison purposes. It is shown that INMF outperforms the conventional batch-mode NMF in all aspects of dynamic background modeling. Although object tracking performance of INMF and the incremental PCA are comparable, INMF is much more robust to illumination changes.
AB - In this paper, an incremental algorithm which is derived from Non-negative Matrix Factorization (NMF) is proposed for background modeling in surveillance type of video sequences. The adopted algorithm, which is called as Incremental NMF (INMF), is capable of modeling dynamic content of the surveillance video and controlling contribution of the subsequent observations to the existing representation properly. INMF preserves additive, parts-based representation, and dimension reduction capability of NMF without increasing the computational load. Test results are reported to compare background modeling performances of batch-mode and incremental NMF in surveillance type of video. Moreover, test results obtained by the incremental PCA are also given for comparison purposes. It is shown that INMF outperforms the conventional batch-mode NMF in all aspects of dynamic background modeling. Although object tracking performance of INMF and the incremental PCA are comparable, INMF is much more robust to illumination changes.
UR - http://www.scopus.com/inward/record.url?scp=58149242250&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:58149242250
SN - 9789728865931
T3 - Proceedings of the 7th International Workshop on Pattern Recognition in Information Systems PRIS 2007; In Conjunction with ICEIS 2007
SP - 107
EP - 118
BT - Proceedings of the 7th International Workshop on Pattern Recognition in Information Systems PRIS 2007; In Conjunction with ICEIS 2007
T2 - 7th International Workshop on Pattern Recognition in Information Systems PRIS 2007; In Conjunction with ICEIS 2007
Y2 - 12 June 2007 through 13 June 2007
ER -