A Benchmark Dataset for Outdoor Foreground/Background Extraction

  • Antoine Vacavant
  • Thierry Chateau
  • Alexis Wilhelm
  • Laurent Lequièvre
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7728)

Abstract

Most of video-surveillance based applications use a foreground extraction algorithm to detect interest objects from videos provided by static cameras. This paper presents a benchmark dataset and evaluation process built from both synthetic and real videos, used in the BMC workshop (Background Models Challenge). This dataset focuses on outdoor situations with weather variations such as wind, sun or rain. Moreover, we propose some evaluation criteria and an associated free software to compute them from several challenging testing videos. The evaluation process has been applied for several state of the art algorithms like gaussian mixture models or codebooks.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Benezeth, Y., Jodoin, P.-M., Emile, B., Laurent, H., Rosenberger, C.: Review and evaluation of commonly-implemented background subtraction algorithms. In: Proc. of IEEE Int. Conf. on Pat. Rec. (2008)Google Scholar
  2. 2.
    Dhome, Y., Tronson, N., Vacavant, A., Chateau, T., Gabard, C., Goyat, Y., Gruyer, D.: A benchmark for background subtraction algorithms in monocular vision: a comparative study. In: Proc. of IEEE Int. Conf. on Image Proc. Theory, Tools and App. (2010)Google Scholar
  3. 3.
    Goyat, Y., Chateau, T., Malaterre, L., Trassoudaine, L.: Vehicle trajectories evaluation by static video sensors. In: Proc. of IEEE Int. Conf. on Intel. Transp. Sys. (2006)Google Scholar
  4. 4.
    Gruyer, D., Royere, C., du Lac, N., Michel, G., Blosseville, J.-M.: SiVIC and RTMaps, interconnected platforms for the conception and the evaluation of driving assistance systems. In: Proc. of World Cong. and Exh. on Intel. Trans. Sys. and Serv. (2006)Google Scholar
  5. 5.
    Hayman, E., Eklundh, J.-O.: Statistical background subtraction for amobile observer. In: Proc. of Int. Conf. on Comp. Vis. (2003)Google Scholar
  6. 6.
    Kaewtrakulpong, P., Bowden, R.: An improved adaptive background mixture model for realtime tracking with shadow detection. In: Proc. of Eur. Work. on Adv. Video Based Surv. Sys. (2001)Google Scholar
  7. 7.
    Kim, K., Chalidabhongse, T.H., Harwood, D., Davis, L.: Real-time foreground-background segmentation using codebook model. Real-time Imag. 11(3), 167–256 (2005)CrossRefGoogle Scholar
  8. 8.
    Lallier, C., Renaud, E., Robinault, L., Tougne, L. In: Proc. of IEEE Int. Conf. on Adv. Video and Signal-based Surv. (2011)Google Scholar
  9. 9.
    Li, L., Huang, W., Gu, I.Y.H., Tian, Q.: Foreground Object Detection from Videos Containing Complex Back-ground. In: Proc. of ACM Multimedia (2003)Google Scholar
  10. 10.
    Prati, A., Mikic, I., Trivedi, M., Cucchiara, R.: Detecting moving shadows: Algorithms and evaluation. IEEE Trans. on PAMI 25(7), 918–923 (2003)CrossRefGoogle Scholar
  11. 11.
    Stauffer, C., Grimson, W.E.L.: Adaptative background mixture models for a real-time tracking. In: Proc. of IEEE Int. Conf. on Comp. Vision and Pat. Rec. (1999)Google Scholar
  12. 12.
    Zivkovic, Z.: Improved adaptive Gaussian mixture model for background subtraction. In: Proc. of IEEE Int. Conf. on Pat. Rec. (2004)Google Scholar
  13. 13.
    Zivkovic, Z., van der Heijden, F.: Efficient adaptive density estimapion per image pixel for the task of background subtraction. Pat. Rec. Let. 27(7), 773–780 (2006)CrossRefGoogle Scholar
  14. 14.
    Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: From error visibility to structural similarity. IEEE Trans. on IP 13(4), 600–612 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Antoine Vacavant
    • 1
    • 2
  • Thierry Chateau
    • 3
  • Alexis Wilhelm
    • 3
  • Laurent Lequièvre
    • 3
  1. 1.ISITClermont Université, Université d’AuvergneClermont-FerrandFrance
  2. 2.CNRS, UMR6284Clermont-FerrandFrance
  3. 3.Pascal InstituteBlaise Pascal University, CNRS, UMR6602Clermont-FerrandFrance

Personalised recommendations