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Authors: Kosmas Dimitropoulos ; Filareti Tsalakanidou and Nikos Grammalidis

Affiliation: Informatics and Telematics Institute, Greece

Keyword(s): Wildfires, Video Surveillance, Flame Detection, 2D Wavelet Analysis, Feature Fusion, SVM Classification.

Related Ontology Subjects/Areas/Topics: Applications and Services ; Camera Networks and Vision ; Color and Texture Analyses ; Computer Vision, Visualization and Computer Graphics ; Image and Video Analysis ; Motion, Tracking and Stereo Vision ; Video Surveillance and Event Detection

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.

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Paper citation in several formats:
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 (VISIGRAPP 2012) - Volume 2: VISAPP; ISBN 978-989-8565-03-7; ISSN 2184-4321, SciTePress, pages 453-456. DOI: 10.5220/0003858104530456

@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 (VISIGRAPP 2012) - Volume 2: VISAPP},
year={2012},
pages={453-456},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003858104530456},
isbn={978-989-8565-03-7},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the International Conference on Computer Vision Theory and Applications (VISIGRAPP 2012) - Volume 2: VISAPP
TI - VIDEO BASED FLAME DETECTION - Using Spatio-temporal Features and SVM Classification
SN - 978-989-8565-03-7
IS - 2184-4321
AU - Dimitropoulos, K.
AU - Tsalakanidou, F.
AU - Grammalidis, N.
PY - 2012
SP - 453
EP - 456
DO - 10.5220/0003858104530456
PB - SciTePress