Gaussian Mixture Background Modelling Optimisation for Micro-controllers

  • Claudio Salvadori
  • Dimitrios Makris
  • Matteo Petracca
  • Jesus Martinez-del-Rincon
  • Sergio Velastin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7431)


This paper proposes an optimisation of the adaptive Gaussian mixture background model that allows the deployment of the method on processors with low memory capacity. The effect of the granularity of the Gaussian mean-value and variance in an integer-based implementation is investigated and novel updating rules of the mixture weights are described. Based on the proposed framework, an implementation for a very low power consumption micro-controller is presented. Results show that the proposed method operates in real time on the micro-controller and has similar performance to the original model.


Wireless Sensor Network Gaussian Mixture Model Double Precision Memory Footprint Gaussian Parameter 
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.
    Piccardi, M.: Background subtraction techniques: a review. In: IEEE International Conference on Systems, Man and Cybernetics, vol. 4, pp. 3099–3104 (2004)Google Scholar
  2. 2.
    Cheung, S.C.S., Kamath, C.: Robust techniques for background subtraction in urban traffic video. In: Visual Communications and Image Processing, vol. 5308, pp. 881–892 (2004)Google Scholar
  3. 3.
    Radke, R.J., Andra, S., Al-Kofahi, O., Roysam, B.: Image change detection algorithms: A systematic survey. IEEE Transactions on Image Processing 14, 294–307 (2005)MathSciNetCrossRefGoogle Scholar
  4. 4.
    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, vol. 2, pp. 2246–2252 (1999)Google Scholar
  5. 5.
    Microchip Technology Inc.: PIC32MX3XX/4XX Family Data Sheet (2008),
  6. 6.
    McFarlane, N.J.B., Schofield, C.P.: Segmentation and tracking of piglets in images. Machine Vision and Applications 8, 187–193 (1995)CrossRefGoogle Scholar
  7. 7.
    Hung, M.H., Pan, J.S., Hsieh, C.H.: Speed up temporal median filter for background subtraction. In: Proceedings of International Conference on Pervasive Computing, Signal Processing and Applications, pp. 297–300 (2010)Google Scholar
  8. 8.
    Rahimi, M., Baer, R., Iroezi, O.I., Garcia, J.C., Warrior, J., Estrin, D., Srivastava, M.: Cyclops: In situ image sensing and interpretation in wireless sensor networks. In: Proceedings of ACM Conference on Embedded Networked Sensor Systems, pp. 192–204 (2005)Google Scholar
  9. 9.
    Iannizzotto, G., La Rosa, F., Lo Bello, L.: A wireless sensor network for distributed autonomous traffic monitoring. In: Conference on Human System Interactions, pp. 612–619 (2010)Google Scholar
  10. 10.
    IEEE Computer Society: IEEE Standard for Floating-Point Arithmetic. Technical report, Microprocessor Standards Committee of the IEEE Computer Society, 3 Park Avenue, New York, NY 10016-5997, USA (2008)Google Scholar
  11. 11.
    Weinland, D., Ronfard, R., Boyer, E.: Free viewpoint action recognition using motion history volumes. Computer Vision and Image Understanding 104, 249–257 (2006)CrossRefGoogle Scholar
  12. 12.
    Tan, B., Zhang, J., Wang, L.: Semi-supervised elastic net for pedestrian counting. Pattern Recognition (2011)Google Scholar
  13. 13.
    Scuola Superiore Sant’Anna and Evidence s.r.l.: SEED-EYE BOARD (2011),

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Claudio Salvadori
    • 1
  • Dimitrios Makris
    • 2
  • Matteo Petracca
    • 3
    • 1
  • Jesus Martinez-del-Rincon
    • 2
  • Sergio Velastin
    • 2
  1. 1.Real-Time Systems LaboratoryScuola Superiore Sant’AnnaPisaItaly
  2. 2.Digital Imaging Research CentreKingston UniversityLondonUnited Kingdom
  3. 3.National Laboratory of Photonic NetworksCNITPisaItaly

Personalised recommendations