Improved Background Mixture Models for Video Surveillance Applications

  • Chris Poppe
  • Gaëtan Martens
  • Peter Lambert
  • Rik Van de Walle
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4843)


Background subtraction is a method commonly used to segment objects of interest in image sequences. By comparing new frames to a background model, regions of interest can be found. To cope with highly dynamic and complex environments, a mixture of several models has been proposed. This paper proposes an update of the popular Mixture of Gaussian Models technique. Experimental analysis shows a lack of this technique to cope with quick illumination changes. A different matching mechanism is proposed to improve the general robustness and a comparison with related work is given. Finally, experimental results are presented to show the gain of the updated technique, according to the standard scheme and the related techniques.


Gaussian Mixture Model Background Model Foreground Object Foreground Pixel Current Pixel 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Dick, A., Brooks, M.J.: Issues in automated visual surveillance. In: Proceedings of International Conference on Digital Image Computing: Techniques and Applications, pp. 195–204 (2003)Google Scholar
  2. 2.
    Wu, J., Trivedi, M.: Performance Characterization for Gaussian Mixture Model Based Motion Detection Algorithms. In: Proceedings of the IEEE International Conference on Image Processing, pp. 97–100. IEEE Computer Society Press, Los Alamitos (2005)Google Scholar
  3. 3.
    Javed, O., Shafique, K., Shah, M.: A Hierarchical Approach to Robust Background Subtraction using Color and Gradient Information. In: Proceedings of the Workshop on Motion and Video Computing, pp. 22–27 (2002)Google Scholar
  4. 4.
    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, pp. 255–261. IEEE Computer Society Press, Los Alamitos (1999)Google Scholar
  5. 5.
    Lee, D.: Online Adaptive Gaussian Mixture Learning for Video Applications. Statistical Methods in Video Processing. LNCS, pp. 105–116 (2004)Google Scholar
  6. 6.
    Zhang, Y., Liang, Z., Hou, Z., Wang, H., Tan, M.: An Adaptive Mixture Gaussian Background Model with Online Background Reconstruction and Adjustable Foreground Mergence Time for Motion Segmentation. In: Proceedings of the IEEE International Conference on Industrial Technology, pp. 23–27. IEEE Computer Society Press, Los Alamitos (2005)CrossRefGoogle Scholar
  7. 7.
    Tian, Y., Lu, M., Hampapur, A.: Robust and Efficient Foreground Analysis for Real-time Video Surveillance. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1182–1187. IEEE Computer Society Press, Los Alamitos (2005)Google Scholar
  8. 8.
    Prati, A., Mikic, I., Trivedi, M.M., Cucchiara, R.: Detecting Moving Shadows: Algorithms and Evaluation. IEEE Transactions on Pattern Analysis and Machine Intelligence 25, 918–923 (2003)CrossRefGoogle Scholar
  9. 9.
    Cucchiara, R., Grana, C., Neri, G., Piccardi, M., Prati, A.: The Sakbot System for Moving Object Detection and Tracking. Video-Based Surveillance Systems - Computer Vision and Distributed Processing, pp. 145–157 (2001)Google Scholar
  10. 10.
    Stauffer, C., Grimson, W.E.L.: Learning Patterns of Activity Using Real-Time Tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 747–757 (2000)CrossRefGoogle Scholar
  11. 11.
    Brown, L.M., Senior, A.W., Tian, Y., Connell, J., Hampapur, A., Shu, C., Merkl, H., Lu, M.: Performance Evaluation of Surveillance Systems Under Varying Conditions. In: Proceedings of IEEE International Workshop on Performance Evaluation of Tracking and Surveillance (2005)

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Chris Poppe
    • 1
  • Gaëtan Martens
    • 1
  • Peter Lambert
    • 1
  • Rik Van de Walle
    • 1
  1. 1.Ghent University - IBBT, Department of Electronics and Information Systems - Multimedia Lab, Gaston Crommenlaan 8, B-9050 Ledeberg-GhentBelgium

Personalised recommendations