Color Based Segmentation and Shape Based Matching of Forest Flames from Monocular Images

This paper proposes novel algorithms for color based segmentation and shape based matching of forest fires from video frames. Fire pixels from the sample images are collected and compose 3D point cloud in RGB color space, based on which Gaussian mixture model is used to build a more precise color model. According to new definition of flame pattern, possible fire region is further segmented with the help of HSV color space. The segmented results are labeled and the ratios of different color pixels are calculated. The candidate fire regions among consecutive frames are then matched using our defined overlapping degree and varying degree of two regions. From the matched candidate fire regions, dynamic features are presented including area, roundness, contour and color distribution, which can be taken as useful features for the recognition of forest flames. Our work has been tested with experiments and proved very valuable to predict forest fire and protect forest resources.