Texture Analysis of Smoke for Real-Time Fire Detection

Authors:
Yu Chunyu;Zhang Yongming;Fang Jun;Wang Jinjun
Affiliations:
-;-;-;-
Venue:
IWCSE '09 Proceedings of the 2009 Second International Workshop on Computer Science and Engineering - Volume 02
Year:
2009

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Transmission: a new feature for computer vision based smoke detection

AICI'10 Proceedings of the 2010 international conference on Artificial intelligence and computational intelligence: Part I
Adaptive estimation of visual smoke detection parameters based on spatial data and fire risk index

Computer Vision and Image Understanding

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Abstract

Since the texture is an important feature of smoke, a novel method of texture analysis is proposed for real-time fire smoke detection. The texture analysis is based on gray level co-occurrence matrices (GLCM) and can distinguish smoke features from other none fire disturbances. For the realization of real-time fire detection, block processing technique is adopted and the computation of texture features is done to every block of image. Neural network is used to classify smoke texture features from none-smoke features and the fire alarm trigger is set according to the total smoke blocks in one frame. The accuracy of the method is discussed as a function of frames in the end.