Vision based forest smoke detection using analyzing of temporal patterns of smoke and their probability models

SunJae Ham; Byoung-Chul Ko; Jae-Yeal Nam

doi:10.1117/12.871995

7 February 2011 Vision based forest smoke detection using analyzing of temporal patterns of smoke and their probability models

SunJae Ham, Byoung-Chul Ko, Jae-Yeal Nam

Author Affiliations +

Proceedings Volume 7877, Image Processing: Machine Vision Applications IV; 78770A (2011) https://doi.org/10.1117/12.871995
Event: IS&T/SPIE Electronic Imaging, 2011, San Francisco Airport, California, United States

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.

Citation Download Citation

SunJae Ham, Byoung-Chul Ko, and Jae-Yeal Nam "Vision based forest smoke detection using analyzing of temporal patterns of smoke and their probability models", Proc. SPIE 7877, Image Processing: Machine Vision Applications IV, 78770A (7 February 2011); https://doi.org/10.1117/12.871995

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