How Far Can You Get by Combining Change Detection Algorithms?

  • Simone Bianco
  • Gianluigi Ciocca
  • Raimondo Schettini
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10484)


Given the existence of many change detection algorithms, each with its own peculiarities and strengths, we propose a combination strategy, that we termed IUTIS (In Unity There Is Strength), based on a genetic Programming framework. This combination strategy is aimed at leveraging the strengths of the algorithms and compensate for their weakness. In this paper we show our findings in applying the proposed strategy in two different scenarios. The first scenario is purely performance-based. The second scenario performance and efficiency must be balanced. Results demonstrate that starting from simple algorithms we can achieve comparable results with respect to more complex state-of-the-art change detection algorithms, while keeping the computational complexity affordable for real-time applications.


Video surveillance Change detection Algorithm combining and selection Genetic Programming CDNET 


  1. 1.
    Al-Sahaf, H., Al-Sahaf, A., Xue, B., Johnston, M., Zhang, M.: Automatically evolving rotation-invariant texture image descriptors by genetic programming. IEEE Trans. Evol. Comput. 21(1), 83–101 (2017)Google Scholar
  2. 2.
    Amelio, A., Pizzuti, C.: An evolutionary approach for image segmentation. Evol. Comput. 22(4), 525–557 (2014)CrossRefGoogle Scholar
  3. 3.
    Bianco, S., Ciocca, G., Schettini, R.: Combination of video change detection algorithms by genetic programming. IEEE Trans. Evol. Comput. (2017).
  4. 4.
    Bianco, S., Ciocca, G.: User preferences modeling and learning for pleasing photo collage generation. Trans. Multimedia Comput. Commun. Appl. 12(1), 1–23 (2015)CrossRefGoogle Scholar
  5. 5.
    Bianco, S., Ciocca, G., Napoletano, P., Schettini, R., Margherita, R., Marini, G., Pantaleo, G.: Cooking action recognition with iVAT: an interactive video annotation tool. In: Petrosino, A. (ed.) ICIAP 2013. LNCS, vol. 8157, pp. 631–641. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-41184-7_64 CrossRefGoogle Scholar
  6. 6.
    Bouwmans, T., Baf, F.E., Vachon, B.: Background modeling using mixture of Gaussians for foreground detection a survey. Recent Pat. Comput. Sci. 1, 219–237 (2008)CrossRefGoogle Scholar
  7. 7.
    Corchs, S., Ciocca, G., Francesca, G.: A genetic programming approach to evaluate complexity of texture images. J. Electron. Imaging 25(6), 061408 (2016)CrossRefGoogle Scholar
  8. 8.
    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
  9. 9.
    Goyat, Y., Chateau, T., Malaterre, L., Trassoudaine, L.: Vehicle trajectories evaluation by static video sensors. In: 2006 Intelligent Transportation Systems Conference, ITSC 2006, pp. 864–869. IEEE (2006)Google Scholar
  10. 10.
    Gregorio, M.D., Giordano, M.: Change detection with weightless neural networks. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 409–413. IEEE (2014)Google Scholar
  11. 11.
    Koza, J.R.: Genetic Programming: On the Programming Of Computers by Means of Natural Selection, vol. 1. MIT Press, Cambridge (1992)zbMATHGoogle Scholar
  12. 12.
    Lacassagne, L., Manzanera, A., Dupret, A.: Motion detection: fast and robust algorithms for embedded systems. In: 2009 16th IEEE International Conference on Image Processing (ICIP), pp. 3265–3268 (2009)Google Scholar
  13. 13.
    Liu, L., Shao, L., Li, X., Lu, K.: Learning spatio-temporal representations for action recognition: a genetic programming approach. IEEE Trans. Cybern. 46(1), 158–170 (2016)CrossRefGoogle Scholar
  14. 14.
    Maddalena, L., Petrosino, A.: The SOBS algorithm: what are the limits? In: 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 21–26. IEEE (2012)Google Scholar
  15. 15.
    McFarlane, N., Schofield, C.: Segmentation and tracking of piglets in images. Mach. Vis. Appl. 8(3), 187–193 (1995)CrossRefGoogle Scholar
  16. 16.
    Noh, S.J., Jeon, M.: A new framework for background subtraction using multiple cues. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds.) ACCV 2012. LNCS, vol. 7726, pp. 493–506. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-37431-9_38 CrossRefGoogle Scholar
  17. 17.
    Oliver, N., Rosario, B., Pentland, A.: A Bayesian computer vision system for modeling human interactions. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 831–843 (2000)CrossRefGoogle Scholar
  18. 18.
    Parks, D.H., Fels, S.S.: Evaluation of background subtraction algorithms with post-processing. In: IEEE 5th International Conference on Advanced Video and Signal Based Surveillance, AVSS 2008, pp. 192–199. IEEE (2008)Google Scholar
  19. 19.
    Sedky, M., Moniri, M., Chibelushi, C.C.: Spectral-360: a physics-based technique for change detection. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 405–408. IEEE (2014)Google Scholar
  20. 20.
    Sobral, A., Vacavant, A.: A comprehensive review of background subtraction algorithms evaluated with synthetic and real videos. Comput. Vis. Image Underst. 122, 4–21 (2014)CrossRefGoogle Scholar
  21. 21.
    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
  22. 22.
    Stauffer, C., Grimson, W.: Adaptive background mixture models for real-time tracking. In: 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 246–252 (1999)Google Scholar
  23. 23.
    Varadarajan, S., Miller, P., Zhou, H.: Spatial mixture of Gaussians for dynamic background modelling. In: 2013 10th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 63–68. IEEE (2013)Google Scholar
  24. 24.
    Wang, B., Dudek, P.: A fast self-tuning background subtraction algorithm. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 401–404. IEEE (2014)Google Scholar
  25. 25.
    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
  26. 26.
    Wang, Y., Jodoin, P.M., Porikli, F., Konrad, J., Benezeth, Y., Ishwar, P.: CDnet 2014: an expanded change detection benchmark dataset. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 393–400. IEEE (2014)Google Scholar
  27. 27.
    Wren, C., Azarbayejani, A., Darrell, T., Pentland, A.: Pfinder: real-time tracking of the human body. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 780–785 (1997)CrossRefGoogle Scholar
  28. 28.
    Yu, M., Rhuma, A., Naqvi, S.M., Wang, L., Chambers, J.: A posture recognition-based fall detection system for monitoring an elderly person in a smart home environment. IEEE Trans. Inf Technol. Biomed. 16(6), 1274–1286 (2012)CrossRefGoogle Scholar
  29. 29.
    Zivkovic, Z., van der Heijden, F.: Efficient adaptive density estimation per image pixel for the task of background subtraction. Pattern Recogn. Lett. 27(7), 773–780 (2006)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Simone Bianco
    • 1
  • Gianluigi Ciocca
    • 1
  • Raimondo Schettini
    • 1
  1. 1.Department of Informatic Systems and CommunicationsUniversity of Milano-BicoccaMilanoItaly

Personalised recommendations