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METHOD FOR DETECTING FOREST FIRE USING SPATIOTEMPORAL BAG-OF-FEATURES AND RANDOM FOREST
METHOD FOR DETECTING FOREST FIRE USING SPATIOTEMPORAL BAG-OF-FEATURES AND RANDOM FOREST
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机译：基于时空特征包和随机森林的森林火灾检测方法
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摘要
The present invention relates to a method for detecting a forest fire using spatiotemporal bag-of-features (BoF) and a random forest and, more particularly, to a method for detecting a forest fire using spatiotemporal bag-of-features and a random forest comprising the steps of: (1) whenever a frame of a video sequence is inputted, detecting the difference between the input frame and a previous frame and, if the difference value exceeds a predetermined first threshold, setting the input frame to a key frame; (2) detecting a moving block from the set key frame; (3) extracting a candidate smoke block from the moving block using a smoke color model; (4) generating BoF from the detected candidate smoke block; and (5) performing learning by a random forest with respect to the generated BoF to determine whether the smoke of the candidate smoke block is real. The method proposed by the present invention can set the key frame from the video sequence, extract the candidate smoke block using the non-parametric smoke color model, extract HOG and HOF from the extracted candidate smoke block to generate BoF as spatiotemporal features from the HOG and the HOF, perform learning by the random forest with respect to the generated BoF, thereby enhancing the capability of detecting a forest fire in real time, reducing a false alarm, and accurately detecting smoke caused by the forest fire.;COPYRIGHT KIPO 2014;[Reference numerals] (AA) Start; (BB) End; (S100) Divide frames forming a video sequence into a plurality of blocks, respectively; (S200) Whenever a frame of the video sequence is inputted, detect the difference between the input frame and a previous frame and, if the difference value exceeds a predetermined first threshold, set the input frame to a key frame; (S300) Detect a moving block from the set key frame; (S400) Extract a candidate smoke block from the moving block using a smoke color model; (S500) Generate bag-of features (BoF) from the detected candidate smoke block; (S600) Perform learning by a random forest with respect to the generated BoF to determine whether the smoke of the candidate smoke block is real
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