Gaussian mixture background modelling optimisation for micro-controllers

C. Salvadori, M. Petracca, D. Makris, J. Martinez-Del-Rincon, S. Velastin

Research output: Chapter in Book/Report/Conference proceedingOther chapter contribution

6 Citations (Scopus)

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.
Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages241-251
Number of pages11
Volume7431 LNCS
DOIs
Publication statusPublished - 2012
EventInternational Symposium on Visual Computing-ISVC - Crete, Greece
Duration: 16 Jul 201218 Jul 2012

Conference

ConferenceInternational Symposium on Visual Computing-ISVC
CountryGreece
CityCrete
Period16/07/201218/07/2012

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    R1118ECI: Centre for Secure Information Technologies (CSIT)

    McCanny, J. V., Cowan, C., Crookes, D., Fusco, V., Linton, D., Liu, W., Miller, P., O'Neill, M., Scanlon, W. & Sezer, S.

    01/08/200930/06/2014

    Project: Research

    Cite this

    Salvadori, C., Petracca, M., Makris, D., Martinez-Del-Rincon, J., & Velastin, S. (2012). Gaussian mixture background modelling optimisation for micro-controllers. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7431 LNCS, pp. 241-251) https://doi.org/10.1007/978-3-642-33179-4_24