Growing Neural Gas Video Background Model (GNG-BM)

  • Munir Shah
  • Jeremiah D. Deng
  • Brendon J. Woodford
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8272)


This paper presents a novel growing neural gas based background model (GNG-BM) for foreground detection in videos. We proposed a pixel-level background model, where the GNG algorithm is modified for clustering the input pixel data and a new algorithm for initial training is introduced. Also, a new method is introduced for foreground-background classification and online model update. The proposed model is rigorously validated and compared with previous models.


Background subtraction online learning growing neural gas video processing and Gaussian mixture model 


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  1. 1.
    Bouwmans, T., Baf, F.E., Vachon, B.: Statistical Background Modeling for Foreground Detection: A Survey, pp. 181–199. World Scientific Publishing (2010)Google Scholar
  2. 2.
    Toyama, K., Krumm, J., Brumitt, B., Meyers, B.: Wallflower: Principles and Practice of Background Maintenance. In: Proceedings of the IEEE International Conference on Computer Vision, ICCV 1999, vol. 1, p. 255. IEEE Computer Society (1999)Google Scholar
  3. 3.
    Stauffer, C., Grimson, W.: Adaptive Background Mixture Models for Real-Time Tracking. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 1999, vol. 2, pp. 246–252. IEEE Computer Society (1999)Google Scholar
  4. 4.
    KaewTraKulPong, P., Rowden, R.: An improved adaptive background mixture model for real-time tracking with shadow detection. In: Proceedings of the Second European Workshop on Advanced Video Based Surveillance Systems, pp. 149–158 (2001)Google Scholar
  5. 5.
    Shah, M., Deng, J., Woodford., B.: Enhancing the Mixture of Gaussians background model with local matching and local adaptive learning. In: Proceedings of the 27th Conference on Image and Vision Computing New Zealand (IVCNZ 2012), pp. 103–108. ACM (2012)Google Scholar
  6. 6.
    Heikkila, M., Pietikainen, M.: A Texture-Based Method for Modeling the Background and Detecting Moving Objects. IEEE Transaction on Pattern Analysis and Machine Intelligence 28(4), 657–662 (2006)CrossRefGoogle Scholar
  7. 7.
    Bouwmans, T., Baf, F.E., Vachon, B.: Background Modeling using Mixture of Gaussians for Foreground Detection - A Survey. Recent Patents on Computer Science 1(3), 219–237 (2008)CrossRefGoogle Scholar
  8. 8.
    Fritzke, B.: A Growing Neural Gas Network Learns Topologies. In: Proceedings of the Advances in Neural Information Processing Systems, pp. 625–632. MIT Press (1995)Google Scholar
  9. 9.
    Martinetz, T., Berkovich, S., Schulten, K.: ‘Neural-gas’ network for vector quantization and its application to time-series prediction. IEEE Transactions on Neural Networks 4(4), 558–569 (1993)CrossRefGoogle Scholar
  10. 10.
    Martinetz, T.: Competitive hebbian learning rule forms perfectly topology preserving maps. In: Proceedings of the International Conference on Artificial Neural Networks (ICANN 1993), pp. 427–434. Springer London (1993)Google Scholar
  11. 11.
    Qin, A.K., Suganthan, P.N.: Robust growing neural gas algorithm with application in cluster analysis. Neural Networks 17(8-9), 1135–1148 (2004)MATHGoogle Scholar
  12. 12.
    Fritzke, B.: A self-organizing network that can follow non-stationary distributions. In: Gerstner, W., Hasler, M., Germond, A., Nicoud, J.-D. (eds.) ICANN 1997. LNCS, vol. 1327, pp. 613–618. Springer, Heidelberg (1997)Google Scholar
  13. 13.
    Evangelio, R.H., Ptzold, M., Sikora, T.: Splitting Gaussians in Mixture Models. In: Proceedings of the 9th IEEE International Conference on Advanced Video and Signal-Based Surveillance, AVSS 2012, pp. 300–305. IEEE Computer Society (2012)Google Scholar
  14. 14.
    Elgammal, A., Duraiswami, R., Harwood, D., Davis, L.: Background and foreground modeling using nonparametric kernel density estimation for visual surveillance. Proceedings of the IEEE 90(7), 1151–1163 (2002)CrossRefGoogle Scholar
  15. 15.
    Hofmann, M., Tiefenbacher, P., Rigoll, G.: Background Segmentation with Feedback: The Pixel-Based Adaptive Segmenter. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition -Change Detection Workshop, pp. 38–43. IEEE (2012)Google Scholar
  16. 16.
    Goyette, N., Jodoin, P., Porikli, F., Konrad, J., Ishwar, P.: A new change detection benchmark dataset. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR) -Workshops, pp. 1–8. IEEE Computer Society (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Munir Shah
    • 1
  • Jeremiah D. Deng
    • 1
  • Brendon J. Woodford
    • 1
  1. 1.Department of Information ScienceUniversity of OtagoDunedinNew Zealand

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