A Novel Fuzzy-Based Smoke Detection System Using Dynamic and Static Smoke Features
Paper in proceeding, 2015

Automatic fire surveillance is an important task for providing emergency response in the event of unexpected fire hazards. Early detection of fire can substantially mitigate the ecological or economical costs associated with a fire disaster. In this regard, as smoke usually always precedes fire, an intelligent smoke detection system is proposed that exploits a Fuzzy Inference System (FIS) in order to aggregate the features of smoke. In addition, robust smoke feature detection algorithms are implemented that take into account both dynamic and static characteristics of smoke. The smoke features include motion, motion orientation (estimated by using the accumulation of motion) for the former and texture for the latter. Experimental results on different video frames show that the proposed smoke detection system has robust performance on detecting the existence of smoke which shows the effectiveness of the proposed smoke detection system.

motion block

fuzzy inference system

smoke detection

smoke features


Yashar Deldjoo

Student at Chalmers

F. Nazary

Islamic Azad University

A. M. Fotouhi

Tafresh University

2015 23rd Iranian Conference on Electrical Engineering

2164-7054 (ISSN)

Vol. 10 729-733
978-1-4799-1971-0 (ISBN)

Subject Categories

Electrical Engineering, Electronic Engineering, Information Engineering



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