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研究生:江珮筠
研究生(外文):Pei-Yun Chiang
論文名稱:基於視覺以時空特徵分析之火災及煙霧偵測系統
論文名稱(外文):Vision-based Fire and Smoke Detection with Spatial-Temporal Features
指導教授:石昭玲連振昌連振昌引用關係
指導教授(外文):Shih, Jau-LingLien, Cheng-Chang
學位類別:碩士
校院名稱:中華大學
系所名稱:資訊工程學系碩士班
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2012
畢業學年度:100
語文別:中文
論文頁數:41
中文關鍵詞:火災及煙霧偵測場景變化偵測高斯混合模型小波分析支持向量機光流法
外文關鍵詞:Fire and smoke detectionscene change detectionGMMwavelet analysisSVMoptical flow
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傳統式的煙霧火災警報器是透過煙霧和溫度感測器來做偵測,必須等到煙霧和火災發生一段時間後才會發生警報。而基於視覺的煙霧及火災偵測系統可以即時偵測到火焰及煙霧並發出警報。在本研究中提出了一個新的基於視覺火災及煙霧偵測法,有效降低錯誤警報的發生率。最近的研究基於視覺煙霧及火災偵測的研究,主要是利用顏色、變動、邊緣、形狀等視覺特徵來偵測火焰及煙霧。但基於視覺的偵測法容易遇到照明及顏色變化的問題。我們所提出的方法結合場景變動偵測、顏色資訊、時空分析及光流變化等特徵來偵測火焰及煙霧。首先我們透過背景相減法偵測出場景變動區域,並透過火焰顏色高斯混合模型來偵測出火焰後選區域;接著透過時空小波分析擷取火焰/煙霧候選區域的運動及空間紋理分布特性;在此,所有上述之視覺特徵被結合成一個即時判斷是否有火焰和煙霧出現的基本規則。最後,我們利用光流特徵與SVM分類器來驗證所偵測到的火焰/煙霧是否有效。從實驗結果可以看到,我們方法的辨識率可達98.0%,偵測效能每秒可處理12張320*240的影像。
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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