outdoor fire detection on the video frames including fire zones close to the fire-like objects recorded by a fixed camera

In this paper, an automatic outdoor fire detection method is proposed for the fire videos recorded by a fixed camera. first, a new set of color rules is introduced to eliminate the nonfire pixels as much as possible while detecting the fire zone pixels completely. next, the texture and flicker effect features are extracted from the detected fire zone, to remove the remainder of nonfire pixels if still any nonfire pixel exists. the texture feature is extracted by using the angular second moment. to extract the flicker effect feature, the time prehistory signal of color components of each fire zone pixel is obtained and passed through a half band high pass filter. finally, the ward classifier clusters the fire features to separate the fire zone pixels from the nonfire. at the various steps of the proposed method, the morphology process is also used to improve the accuracy of fire detection. the proposed method is applied to the 200 different fire videos including the firelike objects. simulation results indicate 6% to 56% improvement on performance of the proposed method in comparison to the similar ones.

Outdoor fire detection on the video frames including fire zones close to the fire-like objects recorded by a fixed camera

In this paper, an automatic outdoor fire detection method is proposed for the fire videos recorded by a fixed camera. First, a new set of color rules is introduced to eliminate the nonfire pixels as much as possible while detecting the fire zone pixels completely. Next, the texture and flicker effect features are extracted from the detected fire zone, to remove the remainder of nonfire pixels if still any nonfire pixel exists. The texture feature is extracted by using the angular second moment. To extract the flicker effect feature, the time prehistory signal of color components of each fire zone pixel is obtained and passed through a half band high pass filter. Finally, the Ward classifier clusters the fire features to separate the fire zone pixels from the nonfire. At the various steps of the proposed method, the morphology process is also used to improve the accuracy of fire detection. The proposed method is applied to the 200 different fire videos including the firelike objects. Simulation results indicate 6% to 56% improvement on performance of the proposed method in comparison to the similar ones.