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Smoke Detection Using GMM and Deep Belief Network

Authors:
Rabeb Kaabi
Moez Bouchouicha
Mounir Sayadi
Eric Moreau

Keywords: smoke detection; GMM; Deep Belief Network

Abstract:
The objective of this work is to develop a deep learning model for classification of smoke and no smoke regions in aerial recorded videos. For that, a deep belief network model was selected and implemented. First, frames were extracted from the provided videos. The Gaussian Mixture Model (GMM) is applied as background estimation algorithm. Then, the Deep Belief Network algorithm is applied to detect the smoke for candidate region. Deep Belief Network was implemented and tested on different datasets. Overall, the obtained results reveal that our implemented model was able to accurately classify smoke and no smoke regions. Through the experiments with input videos obtained from various weather conditions, the proposed algorithms were useful to detect smoke in forests to minimize the damage caused by forest fires onto vegetation, animals and humans, etc.

Pages: 1 to 5

Copyright: Copyright (c) The Government of Tunisia, 2019. Used by permission to IARIA.

Publication date: June 2, 2019

Published in: conference

ISSN: 2519-8432

ISBN: 978-1-61208-716-0

Location: Athens, Greece

Dates: from June 2, 2019 to June 6, 2019