利用雙攝影機取像模組建構一大型環境
監控系統

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姓名

謝明逢(Ming-Feng Hsieh) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

利用雙攝影機取像模組建構一大型環境監控系統
(Construction of a surveillance system for large monitoring spaces by a dual-camera module )

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摘要(中)

現行單一固定式攝影機放置門口、走道或戶外街頭角落等重要位置，監視錄影環境的一舉一動，但由於監視環境空間太大，拍攝目標影像太小，碰到犯罪行為發生時，僅能拍攝到嫌疑犯的身軀，無法提供清晰影像。現行具有PTZ功能之攝影機，雖具有旋轉與放大影像功能，但僅能追蹤某一特定目標物。而吸附式之360度環場攝影機雖可監控追蹤整個環境的目標物，而後驅使PTZ攝影機取得清晰影像，但在某些開放空間場合，不易架設，如：旅館大廳、室外停車場。
基於上述的理由，本研究設計一雙攝影機取像裝置，結合兩種不同功用之攝影機，一為場景攝影機，用來監控整個環境，追蹤所有的目標物，另一為目標物攝影機，具旋轉、放大功能，用來取得目標物之清晰影像。藉由兩支攝影機之擺設位置，與利用特徵點共線之限制條件，設計一演算法，求得雙攝影機影像之對應關係。無需求得目標物在三度空間之位置，也不需事先得知攝影機的參數，直接利用影像處理的技術，自動求得系統兩台攝影機運作所需要的參數，可對多目標物追蹤，同時可取得清晰之目標物特寫鏡頭，並利用所取得之目標物特寫鏡頭，改進追蹤效果。實驗結果證明了本研究所提出之雙攝影機取像裝置是可以用來取得大環境中小目標之清晰影像。

摘要(英)

In traditional surveillance systems, a single CCD camera is installed at the entrance of open space. The problem is that moving targets will be too small to be grabbed so that clearer and recognizable images can not be obtained for identification. As to the CCD camera with panning, tiling, and zooming (PTZ) functions, it can only track one single target.
In this thesis, a dual-camera device is designed to solve the aforementioned problems. Two cameras are installed together for different purposes. One camera called ’’sense camera’’ is used to monitor the whole space and to track multiple targets. The other one called ’’object camera’’ with PTZ functions is used to grab high quality object images. Using some geometrical properties, the two cameras can be automatically calibrated by point correspondence. Without knowing the 3D locations of objects and the camera parameters, the dual-camera device can not only track multiple targets but also obtain their high quality images in the widespreading open spaces. The tracking process can be easily switched from one target to another when multiple objects appear in the monitoring space. In addition, the tracking performance is also improved by using the detail image information grabbed from the object camera. Experiments were conducted on a wide variety of scenes and the experimental results reveal the validity of our proposed approach.

關鍵字(中)

★ 特徵點對應
★ 視訊監控

關鍵字(英)

★ feature point correspondences
★ Video Surveillance

論文目次

Abstract i
摘要 ii
誌謝 iii
目錄 iv
附圖目錄 vi
表格目錄 ix
第一章緒論 1
1.1研究動機 1
1.2相關研究 3
1.3系統簡介 5
1.4論文架構 7
第二章相機幾何 8
2.1 相機參數(Camera Parameters) 8
2.1.1 內部參數 8
2.1.2 外部參數 10
2.2 平面投影幾何 11
2.2.1平面投影轉換理論 11
2.2.2 平面投影轉換矩陣之計算 12
2.2.3 評估平面投影轉換矩陣 14
2.2.4 線的投影轉換 15
2.3 極線幾何(Epipolar Geometry) 15
2.3.1 基本矩陣之計算 16
2.3.2 基本矩陣之評估 19
第三章雙攝影機運作參數 21
3.1 雙攝影機模組架設環境 22
3.2雙攝影機對應參數 24
(1)雙攝影機擺設幾何關係限制條件 27
(2)位向關係約制條件(Ordering Constraint ) 28
(3)極線幾何限制條件(Epipolar Geometry Constraint) 30
(4)相機平面轉換矩陣限制條件(Image Plane Transformation Constraint) 31
(5)共線關係限制條件(Co-linear Constraint) 31
3.3 雙攝影機對應點演算法 33
3.4 目標物攝影機旋轉參數 38
3.5 目標物攝影機縮放(zoom in)參數 41
第四章雙攝影機運作流程 45
4.1場景攝影機目標物偵測 46
4.2場景攝影機目標物追蹤 51
4.2.1 目標物追蹤方法 52
4.2.2 顏色結構(Color Structure Descriptor) 55
4.2.3場景攝影機目標物追蹤結果 57
4.3 雙攝影機的合作追蹤 59
第五章實驗結果 63
5.1系統環境介紹 63
5.2 雙攝影機對應點實驗 64
5.3目標物攝影機旋轉對應點實驗 67
5.4目標物攝影機相近倍率找對應點實驗 71
5.5 雙攝影機運作實驗 72
5.5.1 多人特寫鏡頭紀錄 72
5.5.2 單人特寫鏡頭追蹤 75
第六章結論與未來工作 79
6.1結論 79
6.2 未來工作 80
參考文獻 82

參考文獻

[1] Z. Zhang, “Flexible Camera Calibration By Viewing a Plane From Unknown Orientations”, International Conference on Computer Vision (ICCV'99) , pp. 666-673, September 1999.
[2] Z. Zhang, “A Flexible New Technique for Camera Calibration”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, pp. 1330-1334, November 2000.
[3]C. C. Hu, “Construction of Panormic Images for Monitoring Environment”, IPPR Conference on Computer Vision, Graphics and Image Processing (CVGIP), 2004.
[4] R. I. Hartley, “Kruppa’s equation derived form the fundamental matrix”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no.2, pp. 133-135,1997.
[5] 李昆霖, ”基本矩陣之最佳化與歐基里德重建”, 交通大學資訊科學研究所碩士論文, 2002.
[6] H. P. Moravec, “Toward Automatic Visual Obstacle Avoidance”, Proc. of 5th International Joint Conference on Artifitial Intelligence, pp. 584, August 1977.
[7] C. Harris and M. Stephens, “A Combined Corner and Edge Detector”, Proc.4th Alvery Vision Conf. Manchester, pp. 189-192, August 1988.
[8] S. M. Smith and J. M. Brady, “SUSAN: A New Approach to Low LevelImage Processing”, International Journal of Computer Vision, pp. 45-78, 1997.
[9] 謝銘倫, “室內場景之特徵點擷取與追蹤”, 交通大學資訊科學研究所碩士論文, 2003.
[10] B. Georgescu, P. Meer, “Point matching under large image deformations and illumination changes”, IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 674-688, June 2004.
[11] 鄭智明, “雙攝影機校正研究”, 中華大學資訊工程研究所碩士論文, 2004.
[12] J. W. Hsieh, “Fast stitching algorithm for moving object detection and mosaic construction”, Image and Vison Computing 22, pp. 291~306, 2004.
[13] R. Hartley, “In defense of the eight-point algorithm”, IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 19, pp. 580–593, 1997.
[14] B. Hu, C. Brown, and A. Choi, “Acquiring an environment map through image mosaicking”, Technical Report, Computer Science Department, University of Rochester, 2001.
[15] D. W. Lim, S. H. Choi, and J. S. Jum, “Automated detection of all kinds of violations at a street intersection using real time individual vehicle tracking”, Proceedings. Fifth IEEE Southwest Symposium on Image Analysis and Interpretation, pp. 126-129, April 2002.
[16] 郭大正, “戶外無人管理停車場監控系統”, 中華大學資訊工程碩士論文, 2003.
[17] Y. C. Chung, C. H. Wang, J. M. Wang, S. C. Lin, S. W. Chen, “Integration of Omnidirectional and Movable Cameras for Indoor Surveillance”, IPPR Conference on Computer Vision, Graphics and Image Processing (CVGIP), 2004.
[18] T. Y. Li, C. M. Wang, Y. I. Hung, K. C. Fan, “A Visual Monitoring System Using Wide-Angle Cameras and Pan-Tilt Camera”, IPPR Conference on Computer Vision, Graphics and Image Processing (CVGIP), 2004.
[19] O. Faugeras, Q. T. Luong, and T. Papadopoulo”, The Geometry of Multiple Images : The Laws That Govern the Formation of Multiple Images of a Scene and Some of Their Applications”, March 2001.
[20] M. Pollefeys, “Tutorial on 3D Modeling from Images”, June 26, 2000.
[21] 吳華泰,”整合特徵資訊的區域匹配法研究”，國立台灣大學土木工程研究所碩士論文, 2003.
[21] L. D. Stefano, S. Mattoccia, M. Mola, “A Change-Detection Algorithm Based on Structure and Color”, IEEE Conference on Advanced Video and Signal Based Surveillance, pp.252-259, 2003
[22] F. Chang, C. J. Chen, “A Fast Method for Labeling Connected Components in an image”, IPPR Conference on Computer Vision, Graphics and Image Processing (CVGIP), 2003.
[23] L. M. Fuentes, S. A. Velastin, “People tracking in surveillance applications”, Proceedings 2nd IEEE Int. Workshop on PETS, Kauai, Hawaii, USA,December 9 2001.
[24]劉成祥, “利用視訊資料作人體走勢分析”,中央大學資訊工程研究所碩士論文, 2002.
[25] B. S. Manjunath, P. Salembier, T. Sikora, “Introduction to MPEG-7 Multimedia Content Description Interface”, 2002.
[26] ISO/IEC 15938-3:2001, “Multimedia Content Description Interface – Part 3: Visual”,Version 1.

指導教授

范國清(Kuo-Chin Fan)

審核日期

2005-7-15

推文