Early warning systems are critical in providing
emergency response in the event of unexpected hazards. Cheap
cameras and improvements in memory and computing power
have enabled the design of fire detectors using video surveillance
systems. This is critical in scenarios where traditional smoke
detectors cannot be installed. In such scenarios, it has been
observed that the smoke is visible well before flames can be
sighted. This paper proposes a method to detect fire flame and/or
smoke in real-time by processing the video data generated by
ordinary camera monitoring a scene. The objective of this work
is recognizing and modeling fire shape evolution in stochastic
visual phenomenon. It focuses on detection of fire in image
sequences by applying a hybrid algorithm that depends on
optimizing the structure of a feed forward neural network. Fire
detection experiments using various algorithms were carried.
Results show that the proposed algorithm is very successful in
detecting fire and/or smoke.