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Proceedings Paper

Vision based forest smoke detection using analyzing of temporal patterns of smoke and their probability models
Author(s): SunJae Ham; Byoung-Chul Ko; Jae-Yeal Nam
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Paper Abstract

In general, since smoke appears before flames, smoke detection is particularly important for early fire detection systems. To detect fire-smoke using video camera is a difficult work because main characteristics of a smoke are uncertain, vague, constant patterns of shape and color. Thus, this paper proposes a new fire-smoke detection method, especially forest smoke using analyzing of temporal patterns of smoke and Fuzzy Finite Automata (FFA). To consider the smoke characteristics over time, the temporal patterns of intensity entropy, wavelet energy and motion orientation have been used for generating, multivariate probability density functions (PDFs) are applied Fuzzy Finite Automata (FFA) for smoke verification. The proposed FFA consist of a set of fuzzy states (VH, H, L, VL), and a transition mapping that describes what event can occur at which state and resulting new state. For smoke verification, FFA is most appropriate method in case variables are time-dependent and uncertain. The proposed algorithm is successfully applied to various fire-smoke videos and shows a better detection performance.

Paper Details

Date Published: 7 February 2011
PDF: 7 pages
Proc. SPIE 7877, Image Processing: Machine Vision Applications IV, 78770A (7 February 2011); doi: 10.1117/12.871995
Show Author Affiliations
SunJae Ham, Keimyung Univ. (Korea, Republic of)
Byoung-Chul Ko, Keimyung Univ. (Korea, Republic of)
Jae-Yeal Nam, Keimyung Univ. (Korea, Republic of)


Published in SPIE Proceedings Vol. 7877:
Image Processing: Machine Vision Applications IV
David Fofi; Philip R. Bingham, Editor(s)

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