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.