Skip to main content

Towards Benchmarking Scene Background Initialization

Part of the Lecture Notes in Computer Science book series (LNIP,volume 9281)


Given a set of images of a scene taken at different times, the availability of an initial background model that describes the scene without foreground objects is the prerequisite for a wide range of applications, ranging from video surveillance to computational photography. Even though several methods have been proposed for scene background initialization, the lack of a common groundtruthed dataset and of a common set of metrics makes it difficult to compare their performance. To move first steps towards an easy and fair comparison of these methods, we assembled a dataset of sequences frequently adopted for background initialization, selected or created ground truths for quantitative evaluation through a selected suite of metrics, and compared results obtained by some existing methods, making all the material publicly available.


  • Background initialization
  • Video analysis
  • Video surveillance


  1. Agarwala, A., Dontcheva, M., Agrawala, M., Drucker, S., Colburn, A., Curless, B., Salesin, D., Cohen, M.: Interactive digital photomontage. ACM Trans. Graph. 23(3), 294–302 (2004)

    CrossRef  Google Scholar 

  2. Bouwmans, T.: Traditional and recent approaches in background modeling for foreground detection: An overview. Computer Science Review 1112, 31–66 (2014)

    CrossRef  Google Scholar 

  3. Chen, C.C., Aggarwal, J.: An adaptive background model initialization algorithm with objects moving at different depths. In: 15th IEEE International Conference on Image Processing, 2008. ICIP 2008, pp. 2664–2667 (2008)

    Google Scholar 

  4. Elhabian, S., El Sayed, K., Ahmed, S.: Moving object detection in spatial domain using background removal techniques: State-of-art. Recent Patents on Computer Science 1(1), 32–54 (2008)

    CrossRef  Google Scholar 

  5. Maddalena, L., Petrosino, A.: The SOBS algorithm: what are the limits? In: Proc. CVPR Workshops, pp. 21–26, June 2012

    Google Scholar 

  6. Maddalena, L., Petrosino, A.: Background model initialization for static cameras. In: Bouwmans, T., Porikli, F., Hferlin, B., Vacavant, A. (eds.) Background Modeling and Foreground Detection for Video Surveillance, pp. 3-1-3-16. Chapman & Hall/CRC (2014)

    Google Scholar 

  7. Maddalena, L., Petrosino, A.: The 3dSOBS+ algorithm for moving object detection. Comput. Vis. Image Underst. 122, 65–73 (2014)

    CrossRef  Google Scholar 

  8. Reddy, V., Sanderson, C., Lovell, B.C.: A low-complexity algorithm for static background estimation from cluttered image sequences in surveillance contexts. EURASIP J. Image Video Process. 2011, 1:1–1:14 (2011)

    CrossRef  Google Scholar 

  9. Wang, H., Suter, D.: A novel robust statistical method for background initialization and visual surveillance. In: Narayanan, P.J., Nayar, S.K., Shum, H.-Y. (eds.) ACCV 2006. LNCS, vol. 3851, pp. 328–337. Springer, Heidelberg (2006)

    CrossRef  Google Scholar 

  10. Wang, Z., Simoncelli, E., Bovik, A.: Multiscale structural similarity for image quality assessment. In: Conference Record of the Thirty-Seventh Asilomar Conference on Signals, Systems and Computers, 2004, vol. 2, pp. 1398–1402 (2003)

    Google Scholar 

  11. Yalman, Y., Erturk, I.: A new color image quality measure based on YUV transformation and PSNR for human vision system. Turkish J. of Electrical Eng. & Comput. Sci. 21, 603–612 (2013)

    Google Scholar 

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Lucia Maddalena .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Maddalena, L., Petrosino, A. (2015). Towards Benchmarking Scene Background Initialization. In: Murino, V., Puppo, E., Sona, D., Cristani, M., Sansone, C. (eds) New Trends in Image Analysis and Processing -- ICIAP 2015 Workshops. ICIAP 2015. Lecture Notes in Computer Science(), vol 9281. Springer, Cham.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23221-8

  • Online ISBN: 978-3-319-23222-5

  • eBook Packages: Computer ScienceComputer Science (R0)