VIDEO BASED FLAME DETECTION - Using Spatio-temporal Features and SVM Classification

Kosmas Dimitropoulos, Filareti Tsalakanidou, Nikos Grammalidis

2012

Abstract

Video-based surveillance systems can be used for early fire detection and localization in order to minimize the damage and casualties caused by wildfires. However, reliability of these systems is an important issue and therefore early detection versus false alarm rate has to be considered. In this paper, we present a new algorithm for video based flame detection, which identifies spatio-temporal features of fire such as colour probability, contour irregularity, spatial energy, flickering and spatio-temporal energy. For each candidate region of an image a feature vector is generated and used as input to an SVM classifier, which discriminates between fire and fire-coloured regions. Experimental results show that the proposed methodology provides high fire detection rates with a reasonable false alarm ratio.

Paper Citation

in Harvard Style

Dimitropoulos K., Tsalakanidou F. and Grammalidis N. (2012). VIDEO BASED FLAME DETECTION - Using Spatio-temporal Features and SVM Classification . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2012) ISBN 978-989-8565-03-7, pages 453-456. DOI: 10.5220/0003858104530456

in Bibtex Style

@conference{visapp12,
author={Kosmas Dimitropoulos and Filareti Tsalakanidou and Nikos Grammalidis},
title={VIDEO BASED FLAME DETECTION - Using Spatio-temporal Features and SVM Classification},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2012)},
year={2012},
pages={453-456},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003858104530456},
isbn={978-989-8565-03-7},
}

in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2012)
TI - VIDEO BASED FLAME DETECTION - Using Spatio-temporal Features and SVM Classification
SN - 978-989-8565-03-7
AU - Dimitropoulos K.
AU - Tsalakanidou F.
AU - Grammalidis N.
PY - 2012
SP - 453
EP - 456
DO - 10.5220/0003858104530456