Industry Academic Cooperation Foundation Keimyung University

JaeYeal Nam

ByoungChul Ko

SunJae Ham

Jun Oh Park

Joon Young Kwak

A method of detecting the smoke of a forest fire using the spatiotemporal Bag-of-Features (BoF) of the smoke and a random forest is provided. In the method, whenever each frame of a video sequence is input, a difference between the input frame and a previous frame is detected, and the input frame is set as a key frame if the difference exceeds a predetermined first threshold value. One or more moving blocks are detected in the set key frame. One or more candidate smoke blocks are extracted from the moving blocks using a smoke color model. BoF representations are generated from the detected candidate smoke blocks. Whether smoke of the candidate smoke blocks is actual smoke is determined by performing random forest learning on the generated BoF representation.

Method of detecting smoke of forest fire using spatiotemporal BoF of smoke and random forest

A method for detecting a fire flame using fuzzy finite automata is provided. The fire-flame detection method comprises (1) acquiring an image required for the detection of fire-flame, (2) dividing the image into a number of blocks, (3) extracting a fire-flame candidate block using a brightness distortion of a pixel in the block, (4) detecting a fire-flame candidate region from the fire-flame block using a color probability model, and (5) determining whether the fire-flame candidate region corresponds to a fire-flame via fuzzy finite automata. The fire-flame detection method can detect fire-flames in a variety of fire images with relatively high precision, by establishing a probability model using the brightness distortion and wavelet energy in fire-flame regions with continuous and irregular fluctuation patterns and using the upward motion, and applying the model to fuzzy finite automata.

Method for detecting fire-flame using fuzzy finite automata

A forest fire smoke detection method using random forest classification is provided. In the method, a first reference value is set. For consecutively captured frames, images between the frames are compared, each block, in which a number of pixels, motions of which have been identified, is equal to or greater than the first reference value, is set as a candidate block, and a keyframe is selected. The selected keyframe is compared with at least one frame previous to the keyframe and then a plurality of feature vectors are extracted from the candidate blocks. The extracted feature vectors are learned using different random forest algorithms. Probabilities output to terminal nodes for classes are accumulated, and two first cumulative probability histograms are generated. The two first cumulative probability histograms are averaged, and then a second cumulative probability histogram is generated. A detected state of each candidate block is determined.

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Daegu, South Korea

JaeYeal Nam