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研究生:尤姿晴
研究生(外文):Tzu-Ching Yu
論文名稱:夜間漆黑環境下多重移動光源之追蹤
論文名稱(外文):Tracking of multiple light-sources in a complete drk environment
指導教授:曾逸鴻曾逸鴻引用關係
指導教授(外文):Yi-Hong Tseng
學位類別:碩士
校院名稱:大葉大學
系所名稱:資訊管理學系碩士班
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2007
畢業學年度:95
語文別:中文
論文頁數:64
中文關鍵詞:智慧型視訊監控系統移動光源追蹤移動路徑推估
外文關鍵詞:intelligent video surveillance systemmobile light sources trackingestimation of the track
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  • 收藏至我的研究室書目清單書目收藏:2
現今經濟發展迅速,企業與住家的安全防護愈顯重要,智慧型安全監控系統是近年來資訊技術領域的研究與發展重點。目前大多數監控系統以人力來操作,不僅費時費力且易發生疏漏的情況,因此自動化的視訊監控系統有很大的需求性。另一方面,夜間環境光線不足甚至漆黑一片,相對於日間有很高的犯罪率,如何在夜間做到視訊監控是一個困難但重要的課題。本研究將以平價的網路攝影機做為視訊擷取設備,並以偵測及追蹤漆黑環境下入侵者隨身攜帶的移動式光源,來推估光源持有者的移動路徑。在多重移動光源的追蹤過程中,本研究同時考慮多重光源互相干擾、背景物體的反射影響、持有人或家具遮蔽等情況。主要針對漆黑環境的多重移動光源追蹤與持有人移動路徑推估,並利用30段由各情況模擬的視訊實驗影片中,擷取3225張畫面影像,針對光源位置偵測(正確率94.36%)、光源種類判別(正確率93.39%)、光源遠近及特性判別(正確率93.62%)進行實驗結果與錯誤分析,驗證了本研究所提方法的可行性。
Economy has grown rapidly nowadays; therefore, protection of entrepreneurs and houses has been more and more essential. It makes perfect sense that a smart security monitoring system has become the focus of development and research in the field of information technology during the past few years. At present, most of the monitoring systems are handled by manpower, which is inefficient. It is highly likely that operators overlook some certain thing. As a result, it is highly needed to have an automatic video monitoring system. On the other hand, crime rate is far higher at nighttimes than daytimes. It is certainly a difficult but crucial to monitor at nights. In this study, inexpensive webcams are used as equipment of video capture. Also, they are utilized to detect and track mobile light sources the light source intruders bring in the dark. By doing so, we can estimate the track of the light source holder. In the process of tracking multiple light sources, this research takes many factors into account, including multiple-light interference, effect of reflection of background objects, holders or shades of furniture. However, it mainly focuses on tracking multiple-mobile lights in the dark and on estimation of the track of the holder. By harnessing 3225 images among 30 shots of all kinds of stimulations, we detect the location of light sources (the accurate rate is 94.36%) and judge the type of light source (the accurate rate is 93.39%), the distance and features of the light source the accurate rate 93.62%). The results and analysis of errors prove that methodology in this study works.
中文摘要 ..................... iii
英文摘要 ..................... iv
誌謝辭  ..................... v
內容目錄 ..................... vi
表目錄  ..................... viii
圖目錄  ..................... ix
第一章  緒論................... 1
  第一節  研究背景與動機............ 1
  第二節  研究目的與方法............ 2
  第三節  研究限制............... 6
  第四節  論文架構............... 6
第二章  文獻探討................. 7
  第一節  前景與背景分離............ 7
  第二節  移動物體追蹤............. 9
第三章  前景物體偵測............... 11
  第一節  背景模型建立............. 11
  第二節  背景相減............... 13
  第三節  去除雜訊............... 15
  第四節  前景物體區域調整........... 17
第四章  光源位置偵測............... 20
  第一節  光源種類分類............. 20
  第二節  打火機距離判定............ 23
  第三節  手電筒距離判定............ 28
第五章  多重光源追蹤............... 33
  第一節  物體特徵抽取............. 33
  第二節  前景區域比對............. 34
  第三節  理想情況下光源追蹤.......... 37
  第四節  夜間光源追蹤常見問題......... 39
  第五節  本研究所提之光源追蹤方法....... 45
  第六節  移動軌跡合理性判斷.......... 49
第六章  實驗結果與錯誤分析............ 54
  第一節  實驗結果............... 54
  第二節  錯誤分析............... 56
第七章  結論................... 60
參考文獻 ..................... 61
Badenas, J. ,Sanchiz, J. M. & Pla, F. (2001). Motion-based segmentation and region tracking in image sequences, Pattern Recognition, 34, 661-670.

Chen, T., Wu, Q.H., Rahmani-Torkaman, R. & Hughes, J. (2002). A pseudo top-hat mathematical morphological approach to edge detection in dark regions, Pattern Recognition, 35, no. 1, 199-210.

Colantonio, S., Benvenuti, M., Di Bono, M. G., Pieri, G. & Salvetti, O. (2007). Object tracking in a stereo and infrared vision system, Infrared Physics & Technology, 49, 266–271.

Dornaika, F. & Ahlberg, J. (2006). Fitting 3D face models for tracking and active appearance model training, Image and Vision Computing, 24, 1010–1024.

Fan, J., Yu, J., Fujita, G., Onoye, T., Wu, L. & Shirakawa, I. (2001). Spatiotemporal segmentation for compact video representation, Image Communication, 16, 553 – 566

Gonzalez, R. C. & Woods, R. E. (2002). Digital Image Processing second edition, Prentice Hall, New Jersey.

Kim, E. Y. & Park, S. H. (2006). Automatic video segmentation using genetic algorithms, Pattern Recognition Letters, 27, 1252–1265

Kim, J. B. & Kim, H. J. (2003). Efficient region-based motion segmentation for a video monitoring system, Pattern Recognition Letters, 24, 113–128.

Motamed, C. (2006). Motion detection and tracking using belief indicators for an automatic visual-surveillance system, Image and Vision Computing, 24, 1192–1201.

Ng, M. K., (2000). K-means-type algorithms on distributed memory computer, International Journal of High Speed Computing, vol. 11, 75-91.

Park, S. Y. & Subbarao, M. (2004). Automatic 3D model reconstruction based on novel pose estimation and integration techniques, Image and Vision Computing, 22, 623–635.

Pelleg, D. & Moore, A. (1999). Accelerating exact k-means algorithms with geometric reasoning, In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Diego, CA, 277–281.

Ren, Y., Chua, C. S. & Ho, Y. K. (2003). Statistical background modeling for non-stationary camera, Pattern Recognition Letters, 24, 183–196.

Sclaroff, S. & Isidoro, J. (2003). Active blobs: region-based, deformable appearance models, Computer Vision and Image Understanding, 89, 197–225.

Shapiro, L. G. & Stockman, G. C. (2001). Computer Vision, Prentice Hall, New Jersey.

Singh, M., Mandal, M. K.& Basu, A. (2005). Gaussian and Laplacian of Gaussian weighting functions for robust feature based tracking, Pattern Recognition Letters, 26, 1995–2005.

Su, M. C., & Chou, C. H., (2001). A modified version of the k-means algorithm with a distance based on cluster symmetry, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 6, 674-680.

Suzuki, K., Horiba, I. & Sugie, N. (2003). Neural edge enhancer for supervised edge enhancement from noisy images, IEEE Transactions on Pattern Analysis and Machine Intelligence, 25, 1582-1596

Tissainayagama, P. & Suter, D. (2005). Object tracking in image sequences using point features, Pattern Recognition, 38, 105 – 113.

Tissainayagam, P. & Suter, D. (2003). Contour tracking with automatic motion model switching, Pattern Recognition, 36, 2411–2427.

Tseng, Y. H. & Lin, C. H. (2006). Housebreaker detection by analyzing moving light sources in a dark indoor environment,In Proceedings of the 19th Conference on Computer Vision, Graphics & Image Processing, 720–727, Tauyuan, Taiwan.

Wu, Q. Z. & Jeng, B. (2002). Background subtraction based on logarithmic intensities, Pattern Recognition Letters, 23, 1529–1536.

Yao, Z. & Li, H. (2006). Tracking a detected face with dynamic programming, Image and Vision Computing, 24, 573–580.

Yin, L. & Basu, A. (2001). Generating realistic facial expressions with wrinkles for model-based coding, Computer Vision and Image Understanding, 84, 201–240.

Zhang, Y. & Kambhamettu, C. (2002). 3D headtracking under partial occlusion, Pattern Recognition, 35, 1545–1557.

Zhu, G., Zeng, Q. & Wang, C. (2006). Efficient edge-based object tracking , Pattern Recognition, 39, 2223–2226.
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