Real-Time Wildfire Detection Using Correlation Descriptors

19TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO-2011)(2011)

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Abstract
A video based wildfire detection system that based on spatio-temporal correlation descriptors is developed. During the initial stages of wildfires smoke plume becomes visible before the flames. The proposed method uses background subtraction and color thresholds to find the smoke colored slow moving regions in video. These regions are divided into spatio-temporal blocks and correlation features are extracted from the blocks. Property sets that represent both the spatial and the temporal characteristics of smoke regions are used to form correlation descriptors. An SVM classifier is trained and tested with descriptors obtained from video data containing smoke and smoke colored objects. Experimental results are presented.
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Key words
feature extraction,geophysical image processing,geophysical techniques,image classification,image colour analysis,real-time systems,smoke,support vector machines,video signal processing,wildfires,SVM classifier,color threshold,correlation features,smoke colored objects,smoke colored slow moving regions,smoke plume,spatial characteristic,spatio-temporal blocks,spatio-temporal correlation descriptors,subtraction threshold,temporal characteristic,video based wildfire detection system,
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