The Removal of False Detections from Foreground Regions Extracted Using Adaptive Background Modelling for a Visual Surveillance System

  • Dariusz Frejlichowski
  • Katarzyna Gościewska
  • Paweł Forczmański
  • Adam Nowosielski
  • Radosław Hofman
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8104)


For recent surveillance systems, the false detection removal process is an important step which succeeds the extraction of foreground regions and precedes the classification of object silhouettes. This paper describes the false object removal process when applied to the ’SmartMonitor’ system — i.e. an innovative monitoring system based on video content analysis that is currently being developed to ensure the safety of people and assets within small areas. This paper firstly briefly describes the basic characteristics and advantages of the system. A description of the methods used for background modelling and foreground extraction is also given. The paper then goes on to explain the artefacts removal process using various background models. Finally the paper presents some experimental results alongside a concise explanation of them.


’SmartMonitor’ visual surveillance system video content analysis 


  1. 1.
    Frejlichowski, D., Forczmański, P., Nowosielski, A., Gościewska, K., Hofman, R.: SmartMonitor: An Approach to Simple, Intelligent and Affordable Visual Surveillance System. In: Bolc, L., Tadeusiewicz, R., Chmielewski, L.J., Wojciechowski, K. (eds.) ICCVG 2012. LNCS, vol. 7594, pp. 726–734. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  2. 2.
    Forczmański, P., Frejlichowski, D., Nowosielski, A., Hofman, R.: Current trends in developing of intelligent visual monitoring systems (in Polish). Methods of Applied Computer Science 4/2011(29), 19–32 (2011)Google Scholar
  3. 3.
    Frejlichowski, D., Gościewska, K., Forczmański, P., Nowosielski, A., Hofman, R.: SmartMonitor: recent progress in the development of an innovative visual surveillance system. Journal of Theoretical and Applied Computer Science 6(3), 28–35 (2012)Google Scholar
  4. 4.
    Horprasert, T., Harwood, D., Davis, L.S.: A robust background subtraction and shadow detection. In: Proceedings of the Asian Conference on Computer Vision (2000)Google Scholar
  5. 5.
    Frejlichowski, D.: Automatic Localisation of Moving Vehicles in Image Sequences Using Morphological Operations. In: Proceedings of the 1st IEEE International Conference on Information Technology, Gdańsk 2008, pp. 439–442 (2008)Google Scholar
  6. 6.
    Wang, W., Chen, D., Gao, W., Yang, J.: Modeling Background from Compressed Video. IEEE Transactions on Circuits and Systems for Video Technology 5, 670–681 (2008)CrossRefGoogle Scholar
  7. 7.
    Piccardi, M.: Background Subtraction Techniques: A Review. In: IEEE International Conference on Systems, Man and Cybernetics, vol. 4, pp. 3099–3104 (2005)Google Scholar
  8. 8.
    Zivkovic, Z.: Improved Adaptive Gaussian Mixture Model for Background Subtraction. In: Proceedings of the 17th International Conference on Pattern Recognition, vol. 2, pp. 28–31 (2004)Google Scholar
  9. 9.
    Stauffer, C., Grimson, W.E.L.: Adaptive background mixture models for real-time tracking. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 2–252 (1999)Google Scholar
  10. 10.
    Sen-Ching, S.C.S., Kamath, C.: Robust Techniques for Background Subtraction in Urban Traffic Video. In: Bhaskaran, V., Panchanathan, S. (eds.) Visual Communications and Image Processing, vol. 5308, pp. 881–892 (2004)Google Scholar
  11. 11.
    Javed, O., Shafique, K., Shah, M.: A Hierarchical Approach to Robust Background Subtraction Using Color and Gradient Information. In: Workshop on Motion and Video Computing, pp. 22–27 (2002)Google Scholar
  12. 12.
    Kaewtrakulpong, P., Bowden, R.: An Improved Adaptive Background Mixture Model for Real-Time Tracking with Shadow Detection. In: Proceedings of the 2nd European Workshop on Advanced Video Based Surveillance Systems, Computer Vision and Distributed Processing (2001)Google Scholar
  13. 13.
    Forczmański, P., Seweryn, M.: Surveillance Video Stream Analysis Using Adaptive Background Model and Object Recognition. In: Bolc, L., Tadeusiewicz, R., Chmielewski, L.J., Wojciechowski, K. (eds.) ICCVG 2010, Part I. LNCS, vol. 6374, pp. 114–121. Springer, Heidelberg (2010)CrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2013

Authors and Affiliations

  • Dariusz Frejlichowski
    • 1
  • Katarzyna Gościewska
    • 1
    • 2
  • Paweł Forczmański
    • 1
  • Adam Nowosielski
    • 1
  • Radosław Hofman
    • 2
  1. 1.Faculty of Computer Science and Information TechnologyWest Pomeranian University of Technology, SzczecinSzczecinPoland
  2. 2.Smart Monitor, sp. z o.o.SzczecinPoland

Personalised recommendations