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Optical Engineering

Wildfire smoke detection using temporospatial features and random forest classifiers
Author(s): Byoung Chul Ko; Joon-Young Kwak; Jae-Yeal Nam
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Paper Abstract

We propose a wildfire smoke detection algorithm that uses temporospatial visual features and an ensemble of decision trees and random forest classifiers. In general, wildfire smoke detection is particularly important for early warning systems because smoke is usually generated before flames; in addition, smoke can be detected from a long distance owing to its diffusion characteristics. In order to detect wildfire smoke using a video camera, temporospatial characteristics such as color, wavelet coefficients, motion orientation, and a histogram of oriented gradients are extracted from the preceding 100 corresponding frames and the current keyframe. Two RFs are then trained using independent temporal and spatial feature vectors. Finally, a candidate block is declared as a smoke block if the average probability of two RFs in a smoke class is maximum. The proposed algorithm was successfully applied to various wildfire-smoke and smoke-colored videos and performed better than other related algorithms.

Paper Details

Date Published: 6 February 2012
PDF: 11 pages
Opt. Eng. 51(1) 017208 doi: 10.1117/1.OE.51.1.017208
Published in: Optical Engineering Volume 51, Issue 1
Show Author Affiliations
Byoung Chul Ko, Keimyung Univ. (Korea, Republic of)
Joon-Young Kwak, Keimyung Univ. (Korea, Republic of)
Jae-Yeal Nam, Keimyung Univ. (Korea, Republic of)

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