A novel approach is proposed in this paper for automatic forest fire
detection from video. Based on 3D point cloud of the collected sample fire
pixels, Gaussian mixture model is built and helps segment some possible flame
regions in single image. Then the new specific flame pattern is defined for
forest, and three types of fire colors are labeled accordingly. With 11
static features including color distributions, texture parameters and shape
roundness, the static SVM classifier is trained and filters the segmented
results. Using defined overlapping degree and varying degree, the remained
candidate regions are matched among consecutive frames. Subsequently the
variations of color, texture, roundness, area, contour are computed, then the
average and the mean square deviation of them are obtained. Together with the
flickering frequency from temporal wavelet based Fourier descriptors analysis
of flame contour, 27 dynamic features are used to train the dynamic SVM
classifier, which is applied for final decision. Our approach has been tested
with dozens of video clips, and it can detect forest fire while recognize the
fire like objects, such as red house, bright light and flying flag. Except
for the acceptable accuracy, our detection algorithm performs in real time,
which proves its value for computer vision based forest fire surveillance.
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