Wildfire smoke detection using temporospatial features and random forest classifiers

Ko, ByoungChul; Kwak, Joon-Young; Nam, Jae-Yeal

doi:doi:10.1117/1.OE.51.1.017208

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Optical Engineering / Volume 51 / Issue 1 / Machine Vision, Pattern Recognition

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Wildfire smoke detection using temporospatial features and random forest classifiers

Opt. Eng. 51, 017208 (Feb 06, 2012); http://dx.doi.org/10.1117/1.OE.51.1.017208

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Abstract

References (22)

Citing Articles (15)

ByoungChul Ko, Joon-Young Kwak, and Jae-Yeal Nam

Keimyung University, Department of Computer Engineering1000 Shindang-Dong , Dalseo-Gu, Daegu 704-701, Korea

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.

History

Received Sep 12, 2011
Accepted Nov 23, 2011
Revised Nov 02, 2011
Published online Feb 06, 2012

Digital Object Identifier

http://dx.doi.org/10.1117/1.OE.51.1.017208

Citation

ByoungChul Ko, Joon-Young Kwak and Jae-Yeal Nam, "Wildfire smoke detection using temporospatial features and random forest classifiers", Opt. Eng. 51, 017208 (Feb 06, 2012); http://dx.doi.org/10.1117/1.OE.51.1.017208