A vision based method for detecting lightning in surveillance videos

Lightning is a serious natural calamity all over the world, kills people and wild species, damages human shelters, induces huge forest fire and crashes outdoor camera. Mainly in hilly regions, the purpose of the lightning arrestor fails, seeking vision based solution to detect the lightning in such areas. This paper addresses the detection of lightning in captured videos as natural abruption detection in video frames. The proposed lightning detection technique models the luminance channel of frames in the video as Gray Level Co-occurrence Matrix (GLCM) which defines the arrangement of regions within a frame through statistics. Among various attributes derived from GLCM, contrast and homogeneity which plays vital role in detecting the variations (lightning) between consecutive frames is derived from the GLCM frames constituting the frame feature vector. The difference signal based on the frame feature vector is constructed followed by statistical thresholding which detects the lightning affected frames in the captured video. Experiments on the user collected videos show the high speed and astounding performance of the proposed lightning detection scheme compared to Saturation channel.