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Optical Engineering • Open Access

Fire detection system using random forest classification for image sequences of complex background
Author(s): Onecue Kim; Dong-Joong Kang

Paper Abstract

We present a fire alarm system based on image processing that detects fire accidents in various environments. To reduce false alarms that frequently appeared in earlier systems, we combined image features including color, motion, and blinking information. We specifically define the color conditions of fires in hue, saturation and value, and RGB color space. Fire features are represented as intensity variation, color mean and variance, motion, and image differences. Moreover, blinking fire features are modeled by using crossing patches. We propose an algorithm that classifies patches into fire or nonfire areas by using random forest supervised learning. We design an embedded surveillance device made with acrylonitrile butadiene styrene housing for stable fire detection in outdoor environments. The experimental results show that our algorithm works robustly in complex environments and is able to detect fires in real time.

Paper Details

Date Published: 18 June 2013
PDF: 11 pages
Opt. Eng. 52(6) 067202 doi: 10.1117/1.OE.52.6.067202
Published in: Optical Engineering Volume 52, Issue 6
Show Author Affiliations
Onecue Kim, Pusan National Univ. (Korea, Republic of)
Dong-Joong Kang, Pusan National Univ. (Korea, Republic of)

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