Combining ARF and OR-PCA for Robust Background Subtraction of Noisy Videos

  • Sajid Javed
  • Thierry Bouwmans
  • Soon Ki JungEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9280)


Background subtraction is a fundamental pre-processing step for many computer vision applications. In addition to cope with dynamic background scenes, bad weather conditions such as rainy or snowy environments and global illumination conditions such as light switch on/off are still major challenging problems. Traditional state of the art methods, such as Robust Principal Component Analysis fail to deliver promising results under these worst conditions. This is due to the lack of global pre-processing or post-processing steps, incorrect low-dimensional subspace basis called low-rank matrix estimation, and memory or computational complexities for processing high dimensional data and hence the system does not perform an accurate foreground segmentation. To handle these challenges, this paper presents an input video denoising strategy to cope noisy videos in rainy or snowy conditions. A real time Active Random Field constraint is exploited using probabilistic spatial neighborhood system for image denoising. After that, Online Robust Principal Component Analysis is used to separate the low-rank and sparse component from denoised frames. In addition, a color transfer function is employed between the low-rank and the denoised image for handling abruptly changing lighting conditions, which is a very useful technique for surveillance agents to handle the night time videos. Experimental evaluations, under bad weather conditions using two challenging datasets such as I-LIDS and Change Detection 2014, demonstrate the effectiveness of the proposed method as compared to the existing approaches.


Markov Random Field Image Denoising Color Transfer Robust Principal Component Analysis Foreground Mask 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Wang, B., Dudek, P.: A fast self-tuning background subtraction algorithm. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPR), pp. 401–404. IEEE (2014)Google Scholar
  2. 2.
    Bouwmans, T., Zahzah, E.H.: Robust PCA via Principal Component Pursuit: A review for a comparative evaluation in video surveillance. Computer Vision and Image Understanding, 22–34 (2014)Google Scholar
  3. 3.
    Branch, H.O.S.D.: Imagery library for intelligent detection systems I-LIDS. In: The Institution of Engineering and Technology Conference on Crime and Security, pp. 445–448, June 2006Google Scholar
  4. 4.
    Goyette, N., Jodoin, P., Porikli, F., Konrad, J., Ishwar, P.: a new change detection benchmark dataset. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 1–8, June 2012Google Scholar
  5. 5.
    Candès, E.J., Li, X., Ma, Y., Wright, J.: Robust Principal Component Analysis? Journal of the ACM (JACM) 58(3), 11–37 (2011)CrossRefGoogle Scholar
  6. 6.
    Feng, J., Xu, H., Yan, S.: Online robust PCA via stochastic optimization. In: Advances in Neural Information Processing Systems, pp. 404–412 (2013)Google Scholar
  7. 7.
    Javed, S., Oh, S.H., Sobral, A., Bouwmans, T., Jung, S.K.: OR-PCA with MRF for robust foreground detection in highly dynamic backgrounds. In: Cremers, D., Reid, I., Saito, H., Yang, M.-H. (eds.) ACCV 2014. LNCS, vol. 9005, pp. 284–299. Springer, Heidelberg (2015) Google Scholar
  8. 8.
    Hakim, A.E.A.: A novel approach for rain removal from videos using low-rank recovery. In: Proceedings of the 5th IEEE International Conference on Intelligent Systems, Modelling and Simulation, pp. 13–18 (2014)Google Scholar
  9. 9.
    Barbu, A.: Learning real-time MRF inference for image denoising. In: IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2009, pp. 1574–1581. IEEE (2009)Google Scholar
  10. 10.
    Barbu, A.: Training an active random field for real-time image denoising. IEEE Transactions on Image Processing 18(11), 2451–2462 (2009)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Roth, S., Black, M.: Fields of experts. International Journal of Computer Vision 82(2), 205–229 (2009)CrossRefGoogle Scholar
  12. 12.
    Shah, M., Deng, J.D., Woodford, B.J.: Video background modeling: recent approaches, issues and our proposed techniques. Machine vision and applications 25(5), 1105–1119 (2014)CrossRefGoogle Scholar
  13. 13.
    Hwang, Y., Lee, J.Y., Kweon, I.S., Kim, S.J.: Color transfer using probabilistic moving least squares. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3342–3349. IEEE (2014)Google Scholar
  14. 14.
    Reinhard, E., Ashikhmin, M., Gooch, B., Shirley, P.: Color transfer between images. IEEE Computer graphics and applications 21(5), 34–41 (2001)CrossRefGoogle Scholar
  15. 15.
    Stauffer, C., Grimson, W.E.L.: Adaptive background mixture models for real-time tracking. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2. IEEE (1999)Google Scholar
  16. 16.
    Hofmann, M., Tiefenbacher, P., Rigoll, G.: Background segmentation with feedback: The pixel-based adaptive segmenter. In: 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 38–43 (2012)Google Scholar
  17. 17.
    Kim, K., Chalidabhongse, T.H., Harwood, D., Davis, L.: Background modeling and subtraction by codebook construction. In: 2004 International Conference on Image Processing. ICIP 2004, vol. 5, pp. 3061–3064. IEEE (2004)Google Scholar
  18. 18.
    Wang, R., Bunyak, F., Seetharaman, G., Palaniappan, K.: Static and moving object detection using flux tensor with split gaussian models. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 420–424. IEEE (2014)Google Scholar
  19. 19.
    Lu, X.: A multiscale spatio-temporal background model for motion detection. In: 2014 IEEE International Conference on Image Processing (ICIP), pp. 3268–3271. IEEE (2014)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.School of Computer Science and EngineeringKyungpook National UniversityDaeguRepublic of Korea
  2. 2.Laboratoire MIA (Mathematiques, Image et Applications)Université de La RochelleLa RochelleFrance

Personalised recommendations