A Likelihood-Based Background Model for Real Time Processing of Color Filter Array Videos

  • Vito RenóEmail author
  • Roberto Marani
  • Nicola Mosca
  • Massimiliano Nitti
  • Tiziana D’Orazio
  • Ettore Stella
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9281)


One of the first tasks executed by a vision system made of fixed cameras is the background (BG) subtraction and a particularly challenging context for real time applications is the athletic one because of illumination changes, moving objects and cluttered scenes. The aim of this work is to extract a BG model based on statistical likelihood able to process color filter array (CFA) images taking into account the intrinsic variance of each gray level of the sensor, named Likelihood Bayer Background (LBB). The BG model should be not so computationally complex while highly responsive to extract a robust foreground. Moreover, the mathematical operations used in the formulation should be parallelizable, working on image patches, and computationally efficient, exploiting the dynamics of a pixel within its integer range. Both simulations and experiments on real video sequences demonstrate that this BG model approach shows great performances and robustness during the real time processing of scenes extracted from a soccer match.


Real Time Processing Soccer Match Color Filter Array Smart Camera 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.
    Casares, M., Velipasalar, S., Pinto, A.: Light-weight salient foreground detection for embedded smart cameras. Computer Vision and Image Understanding 114(11), 1223–1237 (2010). special issue on Embedded VisionCrossRefGoogle Scholar
  2. 2.
    Cherian, S., Singh, C., Manikandan, M.: Implementation of real time moving object detection using background subtraction in fpga. In: International Conference on Communications and Signal Processing, ICCSP 2014, pp. 867–871 (2014)Google Scholar
  3. 3.
    D’Orazio, T., Leo, M., Spagnolo, P., Mazzeo, P., Mosca, N., Nitti, M., Distante, A.: An investigation into the feasibility of real-time soccer offside detection from a multiple camera system. IEEE Transactions on Circuits and Systems for Video Technology 19(12), 1804–1818 (2009)CrossRefGoogle Scholar
  4. 4.
    D’Orazio, T., Leo, M., Spagnolo, P., Nitti, M., Mosca, N., Distante, A.: A visual system for real time detection of goal events during soccer matches. Computer Vision and Image Understanding 113(5), 622–632 (2009). computer Vision Based Analysis in Sport EnvironmentsCrossRefGoogle Scholar
  5. 5.
    Godbehere, A., Matsukawa, A., Goldberg, K.: Visual tracking of human visitors under variable-lighting conditions for a responsive audio art installation. In: American Control Conference, ACC 2012, pp. 4305–4312 (2012)Google Scholar
  6. 6.
    Hamid, R., Kumar, R., Hodgins, J., Essa, I.: A visualization framework for team sports captured using multiple static cameras. Computer Vision and Image Understanding 118, 171–183 (2014)CrossRefGoogle Scholar
  7. 7.
    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
  8. 8.
    Oliver, N., Rosario, B., Pentland, A.: A bayesian computer vision system for modeling human interactions. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(8), 831–843 (2000)CrossRefGoogle Scholar
  9. 9.
    Qian, G., Sural, S., Gu, Y., Pramanik, S.: Similarity between euclidean and cosine angle distance for nearest neighbor queries. In: Proceedings of the 2004 ACM Symposium on Applied Computing, SAC 2004, pp. 1232–1237. ACM, New York (2004)Google Scholar
  10. 10.
    Renò, V., Marani, R., D’Orazio, T., Stella, E., Nitti, M.: An adaptive parallel background model for high-throughput video applications and smart cameras embedding. In: Proceedings of the International Conference on Distributed Smart Cameras, ICDSC 2014, pp. 30:1–30:6. ACM, New York (2014)Google Scholar
  11. 11.
    Rittscher, J., Kato, J., Joga, S., Blake, A.: A probabilistic background model for tracking. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1843, pp. 336–350. Springer, Heidelberg (2000) CrossRefGoogle Scholar
  12. 12.
    Sobral, A.: BGSLibrary: An opencv c++ background subtraction library. In: IX Workshop de Visão Computacional (WVC 2013). Rio de Janeiro, Brazil, June 2013Google Scholar
  13. 13.
    Stauffer, C., Grimson, W.: Learning patterns of activity using real-time tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(8), 747–757 (2000)CrossRefGoogle Scholar
  14. 14.
    Suhr, J.K., Jung, H.G., Li, G., Kim, J.: Mixture of gaussians-based background subtraction for bayer-pattern image sequences. IEEE Transactions on Circuits and Systems for Video Technology 21(3), 365–370 (2011)CrossRefGoogle Scholar
  15. 15.
    Yu, X., Farin, D.: Current and emerging topics in sports video processing. In: IEEE International Conference on Multimedia and Expo, ICME 2005, pp. 526–529 (2005)Google Scholar
  16. 16.
    Zivkovic, Z.: Improved adaptive gaussian mixture model for background subtraction. In: Proceedings of the 17th International Conference on Pattern Recognition, ICPR 2004, vol. 2, pp. 28–31, August 2004Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Vito Renó
    • 1
    Email author
  • Roberto Marani
    • 1
  • Nicola Mosca
    • 1
  • Massimiliano Nitti
    • 1
  • Tiziana D’Orazio
    • 1
  • Ettore Stella
    • 1
  1. 1.Institute of Intelligent Systems for Automation, Italian National Research CouncilBariItaly

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