Incremental Non-negative Matrix Factorization for Dynamic Background Modelling

Bilge Gunsel, Serhat S. Bucak, Ozan Gursoy

Abstract

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.

References

  1. Gutchess, D., Trajkovic, M., Cohen-Solal, E., Lyons, D., Jain, A.: A Background Model Initialization Algorithm for Video Surveillance. Proceedings of International Conference on Computer Vision. (2001)
  2. Lee, D.D., Seung, H.S.: Learning the Parts of Objects by Nonnegative Matrix Factorization. Nature, Vol. 401. (1999) 788-791
  3. Pascual-Montano, A., Carazo, J.M., Kochi, K., Lehmann, D., Pascual-Marqui, R.D.: Nonsmooth Nonnegative Matrix Factorization. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 42. (2006) 403-415
  4. Hoyer, P.O.: Non-negative Matrix Factorization with Sparseness Constraints. Journal of Machine Learning Research, Vol. 5. (2004) 1457-1469
  5. Bucak, S.S., Gunsel, B.: Video Content Representation by Incremental Non-negative Matrix Factorization. Submitted to IEEE International Conference on Image Processing. (2007)
  6. Li, Y., Xu, J., Morphett, L., Jacobs, R.: An Integrated Algorithm of Incremental and Robust PCA. Proceedings of IEEE International Conference on Image Processing. (2003)
  7. Oliver, N., Rosario, B., Pentland, A.: A Bayesian Computer Vision System for Modeling Human Interactions. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.22, no.8. (2002) 831-841
  8. PET2001 Surveillance Video Database (http://ftp.pets.rdg.ac.uk/)
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Paper Citation


in Harvard Style

Gunsel B., S. Bucak S. and Gursoy O. (2007). Incremental Non-negative Matrix Factorization for Dynamic Background Modelling . In Proceedings of the 7th International Workshop on Pattern Recognition in Information Systems - Volume 1: PRIS, (ICEIS 2007) ISBN 978-972-8865-93-1, pages 107-116. DOI: 10.5220/0002425501070116


in Bibtex Style

@conference{pris07,
author={Bilge Gunsel and Serhat S. Bucak and Ozan Gursoy},
title={Incremental Non-negative Matrix Factorization for Dynamic Background Modelling},
booktitle={Proceedings of the 7th International Workshop on Pattern Recognition in Information Systems - Volume 1: PRIS, (ICEIS 2007)},
year={2007},
pages={107-116},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002425501070116},
isbn={978-972-8865-93-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 7th International Workshop on Pattern Recognition in Information Systems - Volume 1: PRIS, (ICEIS 2007)
TI - Incremental Non-negative Matrix Factorization for Dynamic Background Modelling
SN - 978-972-8865-93-1
AU - Gunsel B.
AU - S. Bucak S.
AU - Gursoy O.
PY - 2007
SP - 107
EP - 116
DO - 10.5220/0002425501070116