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)

Abstract

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.

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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

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