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研究生(外文):Pei-Yun Chiang
論文名稱(外文):Vision-based Fire and Smoke Detection with Spatial-Temporal Features
指導教授(外文):Shih, Jau-LingLien, Cheng-Chang
外文關鍵詞:Fire and smoke detectionscene change detectionGMMwavelet analysisSVMoptical flow
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Conventional smoke and fire alarms are detected with the smoke and temperature sensors when the smoke and fire have occurred for a time period. Vision-based smoke and fire detection systems can detect the smoke and fire in time. In this study, a novel vision-based fire and smoke detection method is proposed to reduce the false alarm ratio significantly. Recently, vision-based studies applied visual features such as the color, motion, edges, and shape to detect the smoke and fire. However, vision-based detection methods will encounter the problems of the variations of illumination and color. Our proposed method integrates the features of scene change detection, color information, spatial-temporal analysis, and optical flow to detect the fire and smoke simultaneously. First, scene change regions are identified by the background subtraction and then the candidate flame regions are identified by applying fire-colord GMM models. Second, the temporal and spatial wavelet analyses are used to extract the motion and spatial texture distribution characteristics for the fire/smoke regions. Here, all the above-mentioned visual features are integrated with a rule-based judge rule to detect the occurrences of the fire and smoke in time. Finally, we utilize the optical flow features associated with the SVM classifier to verify whether the detected flame/smoke is valid or not. Experimental results show that the recognition rate can approach 98.0% with the efficiency 12 fps.
摘要 i
Abstract ii
致謝 iii
Contents iv
表目錄 vi
圖目錄 vii
第1章 簡介 1
1.1 研究背景與動機 1
1.2 相關文獻探討 3
1.3 研究貢獻 5
1.4 系統架構 5
第2章 火災偵測之特徵 7
2.1 火焰偵測之特徵擷取 7
2.1.1 變動區域偵測 7
2.1.2 火焰顏色像素判斷 9
2.1.3 時間軸小波(Temporal Wavelet)變化分析 11
2.1.4 空間域(Spatial)變化分析 14
2.1.5 光流法(Optical Flow)之運動向量分析 15
2.2 煙霧偵測之特徵擷取 18
2.2.1 變動區域偵測 18
2.2.2 空間域小波(Spatial Wavelet)變化分析 19
2.2.3 光流法(Optical Flow)之運動向量分析 23
2.3 支持向量機(SVM) 24
第3章 火災偵測演算法之特徵決策融合 27
3.1 火焰偵測之特徵決策融合 27
3.2 煙霧偵測之特徵決策融合 28
第4章 實驗結果 30
4.1 火焰偵測之實驗結果 30
4.2 煙霧偵測之實驗結果 34
第5章 結論與未來發展 38
參考文獻 39

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