Skip to main content
SpringerLink
Log in

Single-class SVM for dynamic scene modeling

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

Scene modeling is the starting point and thus the most crucial stage for many vision-based systems involving tracking or recognition. Most of the existing approaches attempt at solving this problem by making some simplifying assumptions such as that of a stationary background. However, this might not always be the case, as swaying trees or ripples in the water often violate these assumptions. In this paper, we present a novel method for modeling background of a dynamic scene, i.e., scenes that contain “non-stationary” background motions, such as periodic motions (e.g., pendulums or escalators) or dynamic textures (e.g., water fountain in the background, swaying trees, or water ripples, etc.). The paper proposes single-class support vector machine (SVM), and we show why it is preferable to other scene modeling techniques currently in use for this particular problem. Using a rectangular region around a pixel, spatial and appearance-based features are extracted from limited amount of training data, used for learning the SVMs. These features are unique, easy to compute and immune to rotation, and changes in scale and illumination. We experiment on a diverse set of dynamic scenes and present both qualitative and quantitative results, indicating the practicality and the effectiveness of the proposed method.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Benezeth, Y., Jodoin, P., Emile, B., Laurent, H., Rosenberger, C.: Review and evaluation of commonly-implemented background subtraction algorithm. In: Proceedings of International Conference on Pattern Recognition (ICPR), pp. 1–4, (2008) (8–12 December)

  2. Calderara, S., Melli, R., Prati, A., Cucchiara, R.: Reliable bacground suppression using complex scenes. In: Proceedings of ACM International Workshop on video surveillance and sensor networks, pp. 211–214 (2006)

  3. Cheng, L., Wang, S., Schuurmans, D.: An online discriminative approach to background subtraction. In: Proceedings of International Conference on Video and Signal Based Surveillance (2006)

  4. Elgammal, A., Duraiswami, R., Harwood, D., Davis, L.S., Duraiswami, R., Harwood, D.: Background and foreground modeling using nonparametric kernel denity for visual surveillance. In: Proceedings of the IEEE, pp. 1151–1163 (2002)

  5. Friedman, N., Russell, S.: Image segmentation in video sequences: a probalistic approach. Uncertainty in Artificial Intelligence, (1997)

  6. Grimson, W., Stauffer, C.: Adaptive background mixture models for real time tracking. In: Proceedings of IEEE Computer Vision and Pattern Recognition (CVPR), pp. 246–252 (1999)

  7. Javed, O., Shah, M.: Tracking and object classification for automated surveillance. In: Proceedings of European Conference on Computer Vision (ECCV) (2002)

  8. Junejo, I., Bhutta, A.A., Foroosh, H.: Scene modeling for object detection using single-class svm. In: Proceedings International Conference on Image processing (ICIP), pp. 1541–1544 (2010)

  9. Karmann K., Brandt A.V.: Moving object recognition using an adaptive background memory. Time-Varying Image Processing and Moving Object Recognitionm, pp. 289–307. Elsevier, Amsterdam (1990)

    Google Scholar 

  10. Ko, T., Soatto, S., Estrin, D.: Background subtraction on distributions. In: Proceedings of European Conference on Computer Vision (ECCV), pp. 276–289 (2008)

  11. Lucas, B., Kanade, T.: An iterative image registration technique with an application to stereo vision. In: Image Understanding Workshop, pp. 121–130 (1981)

  12. Mittal, A., Paragios, N.: Motion-based background subtraction using adaptive kernel density estimation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 302–309 (2004)

  13. Monnet, A., Mittal, A., Paragios, N., Ramesh, V.: Background modeling and subtraction of dynamic scenes. In: Proceedings of International Conference on Computer Vision (ICCV), pp. 1305–1312 (2003)

  14. Oliver N., Rosario B., Pentland A.: A bayesian computer vision system for modeling human interactions. IEEE Trans. Pattern Anal. Mach. Intell. (PAMI) 22, 831–843 (2000)

    Article  Google Scholar 

  15. Pless, R., Larson, J., Siebers, S., Westover, B.: Evaluation of local Pattern Recognition (CVPR), pp. 73–78 (2003)

  16. Prati A., Mikic I., Trivedi M., Cucchiara R.: Detecting moving shadows: algorithms and evaluation. IEEE Trans. Pattern Anal. Mach. Intell. (PAMI) 25(7), 918–923 (2003)

    Article  Google Scholar 

  17. Ren Y., Chua C., Ho Y.: Motion detection with nonstationary background. Mach. Vis. Appl. 13(5–6), 332–343 (2003)

    Article  Google Scholar 

  18. Sheikh Y., Shah M.: Bayesian modeling of dynamic scenes for object detection. IEEE Trans. Pattern Anal. Mach. Intell. (PAMI) 27(11), 1778–1792 (2005)

    Article  Google Scholar 

  19. Stauffer C., Eric W., Grimson W.E.L.: Learning patterns of activity using real-time tracking. IEEE Trans. Pattern Anal. Mach. Intell. (PAMI) 22, 747–757 (2000)

    Article  Google Scholar 

  20. Tian, Y., Lu, M., Hampapur, A.: Robust and efficient foreground analysis for real-time video surveillance. In: Proceedings of IEEE Computer Vision and Pattern Recognition (CVPR), pp. 1182–1187 (2005)

  21. Toyama, K., Krumm, J., Brumitt, B., Meyers, B.: Wallflower: principles and practices of background maintenance. In: Proceedings of International Conference on Computer Vision (ICCV), pp. 255–261 (1999)

  22. Wren C., Azarbayejani A., Darell T., Pentland A.: Pfinder: real-time tracking of human body. IEEE Trans. Pattern Anal. Mach. Intell. (PAMI) 19, 780–785 (1997)

    Article  Google Scholar 

  23. Yu H.: Single-class classification with mapping convergence. Mach. Learn. 61(1–3), 49–69 (2005)

    Article  Google Scholar 

  24. Zhang, S., Yao, H., Liu, S., Chen, X., Gao, W.: A covariance-based method for dynamic background subtraction. In: Proceedings of International Conference on Pattern Recognition (ICPR), pp. 1–4 (2008)

  25. Zhao, T., Aggarwal, M., Kumar, R., Sawhney, H.: Real-time wide area multi-camera stereo tracking. In: Proceedings of IEEE Computer Vision and Pattern Recognition (CVPR), pp. 976–983 (2005)

  26. Zhong, J., Sclaroff, S.: segmenting foreground objects from a dynamic textured background via a robust kalman filter. In: Proceedings of International Conference on Computer Vision (ICCV), pp. 44–50 (2003)

Download references

Author information

Authors and Affiliations

  1. University of Sharjah, Sharjah, UAE

    Imran N. Junejo

  2. University of Central Florida, Orlando, FL, USA

    Adeel A. Bhutta & Hassan Foroosh

Authors
  1. Imran N. Junejo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Adeel A. Bhutta
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hassan Foroosh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Imran N. Junejo.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Junejo, I.N., Bhutta, A.A. & Foroosh, H. Single-class SVM for dynamic scene modeling. SIViP 7, 45–52 (2013). https://doi.org/10.1007/s11760-011-0230-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-011-0230-z

Keywords

Search

Navigation