A Note on Background Subtraction by Utilizing a New Tensor Approach

  • Şahin Işık
  • Kemal Özkan
  • Muzaffer Doğan
  • Ömer Nezih Gerek
Keywords: Common Matrix Approach, Background Modelling, Foreground Detection, Moving Object Detection


This study deals with determining the foreground region by background subtraction based on a new tensor decomposition method. With this aim, the concept of Common Matrix Approach (CMA) is utilized with a purpose of background modelling. The performance of proposed method is validated by making experiments on real videos provided by Wallflower dataset. The obtained results are compared with well-known methods based on subjective on objective evaluation measures. The obtained good results indicate that using the CMA algorithm for background modelling is a simple and effective technique in terms computational cost and implementation. As an eventual result, we have observed that the superior results are determined on complex backgrounds including dynamic objects and illumination variation in image sets.


Download data is not yet available.


T. Bouwmans, Traditional and recent approaches in background modeling for foreground detection: An overview, Computer Science Review, 11 (2014) 31-66.

D. Dushnik, A. Schclar, A. Averbuch, Video segmentation via diffusion bases, arXiv preprint arXiv:1305.0218, (2013).

W. Hu, X. Li, X. Zhang, X. Shi, S. Maybank, Z. Zhang, Incremental tensor subspace learning and its applications to foreground segmentation and tracking, International Journal of Computer Vision, 91 (2011) 303-327.

M.G. Krishna, V.M. Aradhya, M. Ravishankar, D.R. Babu, LoPP: locality preserving projections for moving object detection, Procedia Technology, 4 (2012) 624-628.

Y. Li, J. Yan, Y. Zhou, J. Yang, Optimum subspace learning and error correction for tensors, Computer Vision–ECCV 2010, Springer2010, pp. 790-803.

Z. Zhang, G. Ely, S. Aeron, N. Hao, M. Kilmer, Novel methods for multilinear data completion and de-noising based on tensor-SVD, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition2014, pp. 3842-3849.

S. Ergin, S. Çakir, Ö.N. Gerek, M.B. Gülmezoğlu, A new implementation of common matrix approach using third-order tensors for face recognition, Expert Systems with Applications, 38 (2011) 3246-3251.

K. Toyama, J. Krumm, B. Brumitt, B. Meyers, Wallflower: Principles and practice of background maintenance, Computer Vision, 1999. The Proceedings of the Seventh IEEE International Conference on, IEEE1999, pp. 255-261.

Wallflower Dataset, http://research.microsoft.com/en-us/um/people/jckrumm/WallFlower/TestImages.htm.

S. Ergin, M.B. Gulmezoglu, A novel framework for partition-based face recognition, International Journal of Innovative Computing Information and Control, 9 (2013) 1819-1834.

S. Günal, S. Ergin, M.B. Gülmezoğlu, Ö.N. Gerek, On feature extraction for spam e-mail detection, Multimedia content representation, classification and security, Springer2006, pp. 635-642.

K. Özkan, E. Seke, Image denoising using common vector approach, Image Processing, IET, 9 (2015) 709-715.

K. Özkan, Ş. Işık, A novel multi-scale and multi-expert edge detector based on common vector approach, AEU-International Journal of Electronics and Communications, 69 (2015) 1272-1281.

H. Cevikalp, M. Neamtu, M. Wilkes, A. Barkana, Discriminative common vectors for face recognition, IEEE Transactions on pattern analysis and machine intelligence, 27 (2005) 4-13.

C.R. Wren, A. Azarbayejani, T. Darrell, A.P. Pentland, Pfinder: Real-time tracking of the human body, IEEE Transactions on pattern analysis and machine intelligence, 19 (1997) 780-785.

C. Stauffer, W.E.L. Grimson, Adaptive background mixture models for real-time tracking, Computer Vision and Pattern Recognition, 1999. IEEE Computer Society Conference on., IEEE1999.

A. Elgammal, D. Harwood, L. Davis, Non-parametric model for background subtraction, European conference on computer vision, Springer2000, pp. 751-767.

N.M. Oliver, B. Rosario, A.P. Pentland, A bayesian computer vision system for modeling human interactions, IEEE transactions on pattern analysis and machine intelligence, 22 (2000) 831-843.

D.-M. Tsai, S.-C. Lai, Independent component analysis-based background subtraction for indoor surveillance, IEEE Transactions on Image Processing, 18 (2009) 158-167.

S.S. Bucak, B. Günsel, O. Gursoy, Incremental Non-negative Matrix Factorization for Dynamic Background Modelling, PRIS2007, pp. 107-116.

X. Li, W. Hu, Z. Zhang, X. Zhang, Robust foreground segmentation based on two effective background models, Proceedings of the 1st ACM international conference on Multimedia information retrieval, ACM2008, pp. 223-228.

T. Bouwmans, Subspace learning for background modeling: A survey, Recent Patents on Computer Science, 2 (2009) 223-234.

How to Cite
Şahin Işık, K. Özkan, M. Doğan, and Ömer Gerek, “A Note on Background Subtraction by Utilizing a New Tensor Approach”, IJISAE, pp. 87-91, Dec. 2016.
Research Article