| Summary: | 碩士 === 國立交通大學 === 電控工程研究所 === 101 === In recent years, visual-based fire detection technology in intelligent surveillance systems has received wide attention and research. However, many disturbances can cause problems on a visual-based detection system, such as twinkling light or red moving objects. Establishing a stable and effective fire detection system is still a difficult challenge. This thesis uses the block-based feature extraction method, which easily analyzes local information in the region and reduces the computing data. Local feature of fire block are extracted from the detailed characteristics of fire objects, which are fire color analysis, fire source immobility, and disorder analysis. All three features have great detection rate and filter out different false-positive cases. Analyzing global features with textures and fire area of local candidates selected by local feature analysis further reduces false alarms of the proposed system.
Experimental results show that the fire detection system proposed in this thesis has high detection rate and low false alarm rate under various environments, and prove the reliability and stability in real fire-safety applications. The proposed system is composed of algorithms with low computation, so the system has high possibility for wide use by intelligent monitoring applications based on embedded devices.
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