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.

A Novel Fuzzy-Based Smoke Detection System Using Dynamic and Static Smoke Features

Yashar Deldjoo;Fatemeh Nazary;
2015-01-01

Abstract

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.
2015
23rd Iranian Conference on Electrical Engineering, ICEE 2015
978-1-4799-1971-0
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11589/196537
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