Advertisement

A Pathline-Based Background Subtraction Algorithm

  • Reinier Oves GarcíaEmail author
  • Luis Valentin
  • Carlos Pérez Risquet
  • L. Enrique Sucar
Conference paper
  • 916 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10267)

Abstract

Background subtraction is an important task in video processing and many algorithms are developed for solving this task. The vast majority uses the static behavior of the scene or texture information for separating foreground and background. In this paper we present a novel approach based on the integration of the unsteady vector field embedded in the video. Our method does not learn from the background and neither uses static behavior or texture for detecting the background. This solution is based on motion extraction from the scene by plane-curve intersection. The set of blobs generated by the algorithm are equipped with local motion information which can be used for further image analysis tasks. The proposed approach has been evaluated with a standard benchmark with competitive results against state of the art methods.

Keywords

Background subtraction Motion detection Optical flow Vector field integration 

Notes

Acknowledgments

This work was supported in part by FONCICYT (CONAYT and European Union) Project SmartSDK - No. 272727. Reinier Oves García is supported by a CONACYT Scholarship No.789638

References

  1. 1.
    Beauchemin, S.S., Barron, J.L.: The computation of optical flow. ACM Comput. Surv. (CSUR) 27(3), 433–466 (1995)CrossRefGoogle Scholar
  2. 2.
    Butcher, J.: Runge-Kutta method. Scholarpedia 2(9), 3147 (2007)CrossRefGoogle Scholar
  3. 3.
    Elgammal, A., Harwood, D., Davis, L.: Non-parametric model for background subtraction. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1843, pp. 751–767. Springer, Heidelberg (2000). doi: 10.1007/3-540-45053-X_48 CrossRefGoogle Scholar
  4. 4.
    Farnebäck, G.: Two-frame motion estimation based on polynomial expansion. In: Bigun, J., Gustavsson, T. (eds.) SCIA 2003. LNCS, vol. 2749, pp. 363–370. Springer, Heidelberg (2003). doi: 10.1007/3-540-45103-X_50 CrossRefGoogle Scholar
  5. 5.
    Friedman, N., Russell, S.: Image segmentation in video sequences: a probabilistic approach. In: Proceedings of the Thirteenth Conference on Uncertainty in Artificial Intelligence, UAI 1997, San Francisco, CA, USA, pp. 175–181. Morgan Kaufmann Publishers Inc. (1997)Google Scholar
  6. 6.
    Helman, J.L., Hesselink, L.: Visualizing vector field topology in fluid flows. IEEE Comput. Graph. Appl. 11(3), 36–46 (1991)CrossRefGoogle Scholar
  7. 7.
    Iodoin, J.-P., Bilodeau, G.-A., Saunier, N.: Background subtraction based on local shape. CoRR, abs/1204.6326 (2012)Google Scholar
  8. 8.
    Kim, K., Chalidabhongse, T.H., Harwood, D., Davis, L.: Real-time foreground-background segmentation using codebook model. Real-Time Imaging 11(3), 172–185 (2005)CrossRefGoogle Scholar
  9. 9.
    Maddalena, L., Petrosino, A.: A self-organizing approach to background subtraction for visual surveillance applications. IEEE Trans. Image Process. 17(7), 1168–1177 (2008)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Maddalena, L., Petrosino, A.: The SOBS algorithm: what are the limits? In: CVPR Workshops, pp. 21–26. IEEE Computer Society (2012)Google Scholar
  11. 11.
    Nonaka, Y., Shimada, A., Nagahara, H., Taniguchi, R.: Evaluation report of integrated background modeling based on spatio-temporal features. In: CVPR Workshops, pp. 9–14. IEEE Computer Society (2012)Google Scholar
  12. 12.
    Oliver, N.M., Rosario, B., Pentland, A.P.: A Bayesian computer vision system for modeling human interactions. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 831–843 (2000)CrossRefGoogle Scholar
  13. 13.
    Peng, Z., Laramee, R.S.: Higher dimensional vector field visualization: a survey. In: TPCG, pp. 149–163 (2009)Google Scholar
  14. 14.
    Rajon, D.A., Bolch, W.E.: Marching cube algorithm: review and trilinear interpolation adaptation for image-based dosimetric models. Comput. Med. Imaging Graph. 27(5), 411–435 (2003)CrossRefGoogle Scholar
  15. 15.
    Schick, A., Bäuml, M., Stiefelhagen, R.: Improving foreground segmentations with probabilistic superpixel Markov random fields. In: 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp. 27–31. IEEE, June 2012Google Scholar
  16. 16.
    St-Charles, P.-L., Bilodeau, G.-A., Bergevin, R.: Subsense: a universal change detection method with local adaptive sensitivity. IEEE Trans. Image Process. 24(1), 359–373 (2015)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Stauffer, C., Grimson, W.E.L.: Adaptive background mixture models for real-time tracking. In: CVPR, pp. 2246–2252. IEEE Computer Society (1999)Google Scholar
  18. 18.
    Theisel, H., Weinkauf, T., Hege, H.-P., Seidel, H.-P.: Topological methods for 2d time-dependent vector fields based on stream lines and path lines. IEEE Trans. Vis. Comput. Graph. 11(4), 383–394 (2005)CrossRefGoogle Scholar
  19. 19.
    Toyama, K., Krumm, J., Brumitt, B., Meyers, B.: Wallflower: principles and practice of background maintenance. In: ICCV, pp. 255–261 (1999)Google Scholar
  20. 20.
    Tsai, D.-M., Lai, S.-C.: Independent component analysis-based background subtraction for indoor surveillance. IEEE Trans. Image Process. 18(1), 158–167 (2009)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Wang, H., Schmid, C.: Action recognition with improved trajectories. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3551–3558 (2013)Google Scholar
  22. 22.
    Wang, R., Bunyak, F., Seetharaman, G., Palaniappan, K.: Static and moving object detection using flux tensor with split Gaussian models. In: Proceedings of IEEE CVPR Workshop on Change Detection (2014)Google Scholar
  23. 23.
    Weinkauf, T., Theisel, H.: Curvature measures of 3d vector fields and their applications (2002)Google Scholar
  24. 24.
    Weinkauf, T., Theisel, H.: Streak lines as tangent curves of a derived vector field. IEEE Trans. Vis. Comput. Graph. 16(6), 1225–1234 (2010)CrossRefGoogle Scholar
  25. 25.
    Mingjun, W., Peng, X.: Spatio-temporal context for codebook-based dynamic background subtraction. AEU Int. J. Electron. Commun. 64(8), 739–747 (2010)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Reinier Oves García
    • 1
    Email author
  • Luis Valentin
    • 1
  • Carlos Pérez Risquet
    • 2
  • L. Enrique Sucar
    • 1
  1. 1.Computer Science DepartmentInstituto Nacional de Astrofísica Óptica y ElectrónicaPueblaMexico
  2. 2.Universidad Central “Marta Abreu” de Las VillasSanta ClaraCuba

Personalised recommendations