Machine Vision and Applications

, Volume 25, Issue 6, pp 1573–1584 | Cite as

Background subtraction by combining Temporal and Spatio-Temporal histograms in the presence of camera movement

  • Andrea Romanoni
  • Matteo Matteucci
  • Domenico G. Sorrenti
Original Paper

Abstract

Background subtraction is the classical approach to differentiate moving objects in a scene from the static background when the camera is fixed. If the fixed camera assumption does not hold, a frame registration step is followed by the background subtraction. However, this registration step cannot perfectly compensate camera motion, thus errors like translations of pixels from their true registered position occur. In this paper, we overcome these errors with a simple, but effective background subtraction algorithm that combines Temporal and Spatio-Temporal approaches. The former models the temporal intensity distribution of each individual pixel. The latter classifies foreground and background pixels, taking into account the intensity distribution of each pixels’ neighborhood. The experimental results show that our algorithm outperforms the state-of-the-art systems in the presence of jitter, in spite of its simplicity.

Keywords

Background subtraction Moving camera Temporal background subtraction Spatio-Temporal background subtraction 

References

  1. 1.
    Azzari, P., Stefano, L.D., Bevilacqua, A.: An effective real-time mosaicing algorithm apt to detect motion through background subtraction using a PTZ camera. In: IEEE Conference on Advanced Video and Signal Based Surveillance, 2005. AVSS 2005, pp. 511–516 (2005). doi:10.1109/AVSS.2005.1577321
  2. 2.
    Benezeth, Y., Jodoin, P., Emile, B., Laurent, H., Rosenberger, C.: Review and evaluation of commonly-implemented background subtraction algorithms. In: 19th International Conference on Pattern Recognition, 2008. ICPR 2008, pp. 1–4 (2008). doi:10.1109/ICPR.2008.4760998
  3. 3.
    Bhattacharyya, A.: On a measure of divergence between two multinomial populations. Sankhyā Indian J. Stat. (1933–1960) 7(4), 401–406 (1946)Google Scholar
  4. 4.
    Brutzer, S., Hoferlin, B., Heidemann, G.: Evaluation of background subtraction techniques for video surveillance. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1937–1944 (2011). doi:10.1109/CVPR.2011.5995508
  5. 5.
    Cheung, S.S., Kamath, C.: Robust techniques for background subtraction in urban traffic video. In: Proc. SPIE, Visual communications and image processing, 2004, vol. 5308, pp. 881–898 (2004). doi:10.1117/12.526886
  6. 6.
    Cristani, M., Farenzena, M., Bloisi, D., Murino, V.: Background subtraction for automated multisensor surveillance: a comprehensive review. EURASIP J. Adv. Signal Process 2010, 43:1–43:24 (2010). doi:10.1155/2010/343057 CrossRefGoogle Scholar
  7. 7.
    Elgammal, A., Harwood, D., Davis: Non-parametric model for background subtraction. In: IEEE ICCV Frame-Rate Workshop (1999)Google Scholar
  8. 8.
    Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24(6), 381–395 (1981)CrossRefMathSciNetGoogle Scholar
  9. 9.
    Goyette, N., Jodoin, P., Porikli, F., Konrad, J., Ishwar, P.: Changedetection.net: A new change detection benchmark dataset. In: 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 1–8 (2012). doi:10.1109/CVPRW.2012.6238919
  10. 10.
    Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision, vol. 2. Cambridge University Press, Cambridge (2000)MATHGoogle Scholar
  11. 11.
    Hayman, E., Eklundh, J.: Statistical background subtraction for a mobile observer. In: Proceedings of the Ninth IEEE International Conference on Computer Vision, 2003, pp. 67–74. IEEE, New York (2003)Google Scholar
  12. 12.
    Irani, M., Anandan, P.: A unified approach to moving object detection in 2D and 3D scenes. IEEE Trans. Pattern Anal. Mach. Intell. 20(6), 577–589 (1998). doi:10.1109/34.683770 CrossRefGoogle Scholar
  13. 13.
    Jin, Y., Tao, L., Di, H., Rao, N., Xu, G.: Background modeling from a free-moving camera by multi-layer homography algorithm. In: 15th IEEE International Conference on Image Processing, 2008. ICIP 2008, pp. 1572–1575 (2008). doi:10.1109/ICIP.2008.4712069
  14. 14.
    Kwak, S., Lim, T., Nam, W., Han, B., Han, J.H.: Generalized background subtraction based on hybrid inference by belief propagation and Bayesian filtering. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 2174–2181 (2011). doi:10.1109/ICCV.2011.6126494
  15. 15.
    Li, B., Yuan, B., Miao, Z.: Moving object detection in dynamic scenes using nonparametric local kernel histogram estimation. In: 2008 IEEE International Conference on Multimedia and Expo, pp. 1461–1464 (2008). doi:10.1109/ICME.2008.4607721
  16. 16.
    Migliore, D.A., Matteucci, M., Naccari, M.: A revaluation of frame difference in fast and robust motion detection. In: Proceedings of the 4th ACM International Workshop on Video Surveillance and Sensor Networks, VSSN ’06, pp. 215–218. ACM, New York (2006). doi:10.1145/1178782.1178815
  17. 17.
    Mittal, A., Huttenlocher, D.: Scene modeling for wide area surveillance and image synthesis. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2000, vol. 2, pp. 160–167 (2000). doi:10.1109/CVPR.2000.854767
  18. 18.
    Piccardi, M.: Background subtraction techniques: a review. In: 2004 IEEE International Conference on Systems, Man and Cybernetics, vol. 4, pp. 3099–3104 (2004). doi:10.1109/ICSMC.2004.1400815
  19. 19.
    Ren, Y., Chua, C.S., Ho, Y.K.: Statistical background modeling for non-stationary camera. Pattern Recogn. Lett. 24(1–3), 183–196 (2003). doi:10.1016/S0167-8655(02)00210-6. http://www.sciencedirect.com/science/article/pii/S0167865502002106
  20. 20.
    Sawhney, H., Guo, Y., Asmuth, J., Kumar, R.: Independent motion detection in 3D scenes. In: Proceedings of the Seventh IEEE International Conference on Computer Vision, 1999, vol. 1, pp. 612–619 (1999). doi:10.1109/ICCV.1999.791281
  21. 21.
    Sheikh, Y., Javed, O., Kanade, T.: Background Subtraction for freely moving cameras. In: 2009 IEEE 12th International Conference on Computer Vision, vol. 2, pp. 1219–1225 (2009). doi:10.1109/ICCV.2009.5459334
  22. 22.
    Shi, J., Tomasi, C.: Good features to track. In: 1994 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1994. Proceedings CVPR’94, pp. 593–600. IEEE, New York (1994)Google Scholar
  23. 23.
    Stauffer, C., Grimson, W.: Adaptive background mixture models for real-time tracking. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1999, vol. 2, pp. 2 vol. (xxiii+637+663) (1999). doi:10.1109/CVPR.1999.784637
  24. 24.
    Sugaya, Y., Kanatani, K.: Extracting Moving Objects from a Moving Camera Video Sequence. In: 10th Symposium on Sensing via Image Information, pp. 279–284 (2004)Google Scholar
  25. 25.
    Trucco, E., Verri, A.: Introductory Techniques for 3-D Computer Vision, vol. 93. Prentice Hall, New York (1998)Google Scholar
  26. 26.
    Yuan, C., Medioni, G., Kang, J., Cohen, I.: Detecting motion regions in the presence of a strong parallax from a moving camera by multiview geometric constraints. IEEE Trans. Pattern Anal. Mach. Intell. 29(9), 1627–1641 (2007). doi:10.1109/TPAMI.2007.1084 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Andrea Romanoni
    • 1
  • Matteo Matteucci
    • 1
  • Domenico G. Sorrenti
    • 2
  1. 1.Politecnico di Milano, DEIBMilanItaly
  2. 2.Universitá degli Studi Milano-Bicocca, DISCoMilanItaly

Personalised recommendations