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An Intelligent System for False Alarm Reduction in Infrared Forest-Fire Detection

By Begoña C. Arrue Aníbal Ollero and J. Ramiro Martinez De Dios

Abstract

disasters, creating economical and ecological damage as well as endangering people’s lives. Heightened interest in automatic surveillance and early forest-fire detection has taken precedence over traditional human surveillance because the latter’s subjectivity affects detection reliability, which is the main issue for forest-fire detection systems. In current systems, the process is tedious, and human operators must manually validate many false alarms. Our approach—the False Alarm Reduction system—proposes an alternative realtime infrared–visual system that overcomes this problem. The FAR system consists of applying new infrared-image processing techniques and Artificial Neural Networks (ANNs), using additional information from meteorological sensors and from a geographical information database, taking advantage of the information redundancy from visual and infrared cameras through a matching process, and designing a fuzzy expert rule base to develop a decision function. Furthermore, the system provides the human operator with new software tools to verify alarms. Drawbacks to other systems Researchers have applied many technologies to forest surveillance, and early forest

Topics: TOOLS, NEURAL NETWORKS, AND EXPERT FUZZY RULES TO DETECT FOREST FIRES IN OPEN AREAS. THE TECHNIQUES APPLIED ARE BASED ON IMA, VISUAL INFRARED IMAGE MATCHING, MEMORY OF PREVIOUS EVENTS, METEOROLOGICAL AND GEOGRAPHICAL INFORMATION, MOTION, SIZE, AND LOCATION
Year: 2008
OAI identifier: oai:CiteSeerX.psu:10.1.1.134.1263
Provided by: CiteSeerX
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