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  • 學位論文

適用於多種解析度之嚴謹小火焰智慧型視訊偵測演算法的開發與設計

Robust Little Flame Detection on Real-Time Video Surveillance System

指導教授 : 郭忠義

摘要


近年來,隨著智慧型安全監控系統的蓬勃發展,對於火焰偵測的研究也如雨後春筍般的萌芽,日漸受到重視。以往火災偵測系統皆是依靠裝置各種感應器於環境中,當火災發生時,發出警報再由人力親至現場確認。若是依靠視訊監控系統,不僅對於火災的反應時間較為快速,也不需由人力親自奔波至現場確認情況,並可記錄下感測器所無法提供的各種火災訊息。本論文提出一套在各種影像解析度視訊監控系統下,利用火焰特徵資訊自動地偵測出火焰前期的小型火苗與火焰燃燒旺盛時火焰之方法。研究中採用動量偵測與YCbCr色彩空間的搭配來萃取出前景物體,縮小偵測的範圍,在動量偵測中為避免影像解析度不同所造成的背景雜訊影響,因此以縮圖及建立區間背景邊緣模型的方法來取代形態學的處理。再搭配火焰各種特徵,角點閃爍頻率、緊緻度、增長率及填充率判斷出前景物體是否為火焰物件。本論文方法改善了火焰區域擷取的完整度,並且利用火焰各種特徵嚴謹的條件判斷,降低誤偵測的機率。本實驗環境設定在各種影像解析度的複雜環境當中,無論影像解析度高或低、室內室外、及干擾物體的存在,例如:室外汽機車的經過、紅色飄動的旗幟,利用本論文的方法,能夠準確地判斷出火災的發生,並且排除掉沒有危險性的人為控制火焰。

並列摘要


In current era, there are various kinds of sensor used to detect the occurrence of fire. When a fire disaster occurs, security needs to go to the place and assesses the situation. In contrast, video-based fire detection system not only gives a faster response time but also provides with some fire information. This information help security to verify the fire alarm. This study proposes a method to detect the little flame in the early stage of fire combustion. The foreground object was extracted by motion detection and YCbCr color clues. To avoid the noise of motion detection in different resolution videos, background edge is used to eliminate noise instead of morphology. Next, with the help of fire characteristics, the foreground object is identified. A fire object is determined by compactness, corner flicker rate, and growth rate. The experiment can be applied to any resolution video and complex scene, both indoors and outdoors, such as squares, where people walk around and vehicles pass by. The outcome of experiment, using this proposed method, can detect the fire object accurately and exclude the undangerous fire.

參考文獻


[1] Hong Jin, and Rong-Biao Zhang," A Fire and Flame Detecting Method Based on Video," in International Conference on Machine Learning and Cybernetics, vol.4, pp. 2347-2352, 2009.
[2] Wen-Bing Horng, Jian-Wen Peng, and Chih-Yuan Chen," A New Image-Based Real-Time Flame Detection Method Using Color Analysis," in IEEE on Networking, Sensing and Control, pp. 100- 105, 2005.
[4] Dengyi Zhang, Jinming Zhao, Jianhui Zhao, Shizhong Han, Zhong Zhang, Chengzhang Qu, and Youwang Ke," A New Color-based Segmentation Method for Forest Fire from Video Image," in International Seminar on Future BioMedical Information Engineering, pp. 41-44, 2008.
[5] Thou-Ho Chen, Ping-Hsueh Wu, and Yung-Chuen Chiou," An Early Fire-Detection Method Based on Image Processing," in International Conference on Image Processing, vol. 3, pp. 1707- 1710, 2004.
[6] T. Celik, H. Ozkaramanli, and H. Demirel," Fire Pixel Classification using Fuzzy Logic and Statistical Color Model," in IEEE International Conference on Speech and Signal Processing, vol. 1, pp. 1205-1208, 2007.

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