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研究生:邱詩婷
論文名稱:基於影像處理技術之煙霧偵測
論文名稱(外文):Smoke Detection Based on Image Visual Features
指導教授:吳炳飛
學位類別:碩士
校院名稱:國立交通大學
系所名稱:電控工程研究所
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:2011
畢業學年度:99
語文別:中文
論文頁數:61
中文關鍵詞:煙霧偵測煙霧特徵電腦視覺影像辨識
外文關鍵詞:smoke detectionfeaturescomputer visionpattern recognition
相關次數:
  • 被引用被引用:1
  • 點閱點閱:668
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
監控系統種類繁多,本論文著重於煙霧偵測。煙霧是個看得見,卻摸不清的東西,不論是運動方向或運動模式,都是無法明確的表達、闡述。本研究將以影像的資訊,探討煙霧的特徵。
本論文的主要架構為「煙霧特徵介紹」、「演算法介紹」以及「實驗結果分析」。在特徵介紹中,為了使系統能夠適應各種不同的環境,本論文著重於煙霧與背景的關聯性,探討煙霧的產生前後,對於畫面以及背景的影響,並設計各種規範,實現自動化煙霧偵測系統。
在於實驗結果分析,本研究設計各種實驗場景,由自然光的環境到光線不勻的隧道,由室外到室內,由白天到夜間,並與先前的相關研究比較分析,探討其相異之處。

Vision is one of the most direct receptors. The current surveillance that is still a human equipped with video monitors. Highly homogeneous work like this, the human beings will be out of attentions and become poor efficiency. This paper will introduce an automatic monitor system to complete the associated mission. It will save on the cost and also have related work efficiently without an exhausted condition.
There are lots of kinds of monitoring systems. This paper focuses on smoke detection. In this paper consists of "smoke features introduction", "Introduction to smoke detection algorithm" and "experimental results". About introduction, in order to adapt to different environments, our work focuses on the correlations of smog and background. Then design the rules to implement smoke detection systems. Analysis of experimental results, we design various experimental scenes, the natural environment, Muja tunnel, outdoor, indoor, and nighttime. We will take a look of the previous related research and analysis, and discuss the differences.

基於影像處理技術之煙霧偵測 I
摘要 I
ABSTRACT II
誌謝 III
目錄 IV
表目錄 VI
第一章、 緒論 1
第一節、 研究動機 1
第二節、 研究背景 2
第三節、 實驗環境說明 4
第四節、 論文架構 5
第二章、 煙霧偵測演算法 6
第一節、 煙霧特徵介紹 7
第二節、 煙霧偵測 32
第三節、 煙霧追蹤 49
第三章、 實驗結果分析 53
第一節、 實驗結果分析 53
第二節、 成果比較與分析 56
第四章、 結論與未來展望 58
參考文獻 59
[1] B. Lee, D. Han, “Real-Time Fire Detection Using Camera Sequence Image in Tunnel Environment,” Proceedings of the intelligent computing 3rd International Conference on Advanced intelligent computing theories and applications, pp.1209-1220.
[2] C.C. Ho, "Real-Time Video-Based Fire Smoke Detection System," IEEE International Conference on Advanced Intelligent Mechatronics, pp.1845-1850
[3] C.Y. Lee, C.T. Lin, C.T. Hong, “Spatio-Temporal Analysis in Smoke Detection,” IEEE International Conference on Signal and Image Processing Applications, pp.80-83, 2009.
[4] C.H. Chen, Y.H. Yin, S.F. Huang, Y.T. Ye, “The Smoke Detection for Early Fire-Alarming System Base on Video Processing,” IEEE International Conference on Intelligent Information Hiding and Multimedia Signal Processing, pp.427-430, 2006.
[5] D. Han, B. Lee, “Development of Early Tunnel Fire Detection Algorithm Using the Image Processing”, Advances in Visual Computing, pp.39-48.
[6] D. Xie, R. Tong, H. Wu, “A Method to Distinguish the Fire And Flickering Vehicle Light,” Proceedings of the 2009 WRI World Congress on Computer Science and Information Engineering, Vol. 6, pp.355-359, 2009.
[7] F. Yuan, "A Fast accumulation motion orientation model based on integral image for video smoke detection," Pattern Recognition Letters 29(2008), pp.925-932.
[8] H. Maruta, Y. Kato, A. Nakamura, F. Kurokawa, “Smoke Detection in open areas using its texture features and time series properties,” IEEE International Symposium on Industrial Electronics, pp.1904-1908, 2009.
[9] J. Yang, F. Chen, W. Zhang, “Visual-based Smoke Detection using Support Vector Machine,” IEEE International Conference on Natural Computation, pp.301-305, 2008.
[10] K. Ma, L. Zhu, K. Wu, “Fire Smoke Detection in video images Using Kalman Filter and Gaussian Mixture Color model,” International Conference on Artificial Intelligence and Computational Intelligence, pp.484-487, 2010.
[11] M. Kandil, M. Salama, S. Rashad, “Fire Detection Using a Dynamically Developed Neural Network,” ELMAR, pp.97-100, 2010.
[12] M. Kandil, M. Salama, “A New Hybrid Algorithm for Fire Vision Recognition,” IEEE International Conference on EUROCON, pp.1460-1466, 2009.
[13] M. Wirth, R. Zaremba, "Flame region detection on histogram backprojection," Canadian Conference on Computer and Robot Vision, pp.167-174, 2010.
[14] Shindang-dong, Dalseo-gu, “Early fire detection algorithm based on irregular patterns of flames and hierarchical Bayesian Networks,” Fire Safety Journal 45(2010), pp. 262-270.
[15] T. X. Truong, J. M. Kim, "An Early Smoke Detection System based on Motion Estimation," International Forum on Strategic Technology, pp. 437-440, 2010.
[16] T. Celik, H. Ozkaramanli, H. Demirel, “Fire and smoke detection without sensors: Image Processing Based Approach,” 15th European Signal Processing Conference, pp.147-158.
[17] Y. Chunyu, Z. Yongming, F. Jun, W. Jinjun, “Video Smoke Recognition Based on Optical Flow,” IEEE International Conference on Advanced Computer Control, pp.16-21, 2010
[18] Z. Xu, J. Xu, “Automatic Fire Smoke Detection Based on Image Visual Features,” CISW, pp.316-319, 2007.
[19] Z. Wei, X. Wang, W. An, J. Che, “Target-Tracking Based Early Fire Smoke Detection in Video,” IEEE International Conference on Image and Graphics, pp.172-176, 2009.


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