Image fire detection based on independent component analysis and support vector machine

Abstract

Image-based fire detection can effectively solve the problems of large space fire detection contactlessly and rapidly.It is a new research direction in fire detection.Its essential issue is the classification of flames and disruptors.The ordinary detection methods are to extract one or a few characteristics of the flame in the image as a basis for identification.The disadvantages are to need a large number of experiential thresholds and the lower recognition rate by the inappropriate feature selection.Considering the entire characteristics of fire flame,a flame detection method based on Independent Component Analysis(ICA) and Support Vector Machine(SVM) was proposed.Firstly,a series of frames were pre-processed in RGB space.And suspected target areas were extracted depending on the flickering feature and fuzzy clustering analysis.Then the flame image features were described with ICA.Finally,SVM model was used in order to achieve flame recognition.The experimental result shows that the proposed method improves the accuracy and speed of image fire detection in a variety of fire detection environments.