| Summary: | 碩士 === 國立交通大學 === 電機與控制工程系所 === 97 === Visual-based smoke detection techniques in surveillance systems have been studied for years. However, given an image in open or large spaces with typical smoke and disturbances of commonly moving objects such as pedestrians or vehicles, robust and efficient smoke detection is still a challenging problem. In this paper, we present a novel and reliable framework for automatic smoke detection. Automatic monitoring systems use a stochastic approximation procedure, which is used to recursively estimate the parameters of the Gaussian mixture model and construct a background image for foreground segmentation. Next, spatial and temporal characteristics are analyzed of the candidate regions in the video sequences. It exploits three features: edge blurring, the gradual change of energy and the gradual change of chromatic configuration. In order to gain proper generalization ability with respect to sparse training samples, the three features are combined using a support vector machine based classifier. This system has been run more than 6 hours in various conditions to verify the reliability of fire safety in the real world. The results obtained from this novel approach would provide better insight to operators in the field of smoke detection to handle the problems of high false alarm rate and long reaction time.
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