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研究生:楊新華
論文名稱:雙攝影機追蹤系統
論文名稱(外文):Dual Camera Tracking System
指導教授:張文鐘
學位類別:碩士
校院名稱:國立交通大學
系所名稱:電信工程系所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
畢業學年度:96
語文別:英文
論文頁數:73
中文關鍵詞:背景學習擷取前景監控系統
外文關鍵詞:background modelingforeground segmentationvideo surveillanceBayes model
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為了能追蹤移動物體並且紀錄特寫鏡頭做為備查,我們實作一個雙攝影機追蹤系統。這個系統可以分成兩個階段。第一階段,目標追蹤子系統從視訊攝影機讀取視訊資料,然後擷取前景,偵測及追蹤物體。我們使用背景學習、變化偵測、變化分類和前景分割的方法來擷取前景。我們使用輪廓和物體追蹤技術來偵測及追蹤物體。第二階段,座標轉換子系統使用planar homography mapping的方法轉換物體在視訊攝影機的座標到PTZ的座標。為了讓PTZ能追蹤移動物體並且給予特寫鏡頭,座標轉換子系統根據物體的座標及大小,自動地調整 PTZ的參數。
In order to capture a high resolution view of interested target region, we implement a dual camera tracking system that has two stages. In the first stage, the input to a target tracking subsystem is video streams from a single web camera. The subsystem analyzes the video content by extracting the foreground from the background, detecting and tracking the objects. The foreground is separated from the background by using background learning, change detection, change classification and foreground object segmentation methods. The objects are detected and tracked by using contouring and blobs tracking techniques. In the second stage, coordinate transformation subsystem transforms the coordinate of object on webcam coordinate system to PTZ coordinate system by using planar homography mapping method. In order to capture a high resolution view of interested target region from PTZ, the coordinate transformation subsystem adjusts automatically the PTZ parameters based on the coordinate and size of the object on PTZ coordinate system.
I. 摘要 i
II. Abstract ii
III. Acknowledgement iii
IV. Table of Contents iv
V. List of Figures vi
1. Introduction 1
1.1. Motivation 1
1.2. System Overview 3
1.3. Thesis Outline 4
2. Theory of Dual Camera Tracking System 5
2.1. Target Tracking System 5
2.1.1. Block-based or Pixel-based Foreground/background Segmentation 7
2.1.2. Simple Foreground/background Segmentation 21
2.1.3. Blobs Tracking 23
2.2. Coordinate Transformation System 27
2.2.1. Coordinate System Changes and Rigid Transformations 27
2.2.2. Intrinsic camera parameters 30
2.2.3. Homography Derivation 34
2.2.4. Homography Calculation 37
2.2.5. Generation of Pan, Tilt and Zoom Tables 40
2.2.6. Execution of Coordinate Transformation System 42
3. Experimental Results 44
3.1. Test application and system 44
3.2. Result of Target Tracking System 46
3.3. Module of Homography 49
3.4. Result of Whole System 50
4. Conclusion and Future Works 53
5. References 54
6. Appendix of Programming 56
6.1. Simple Capturing Video from A Webcam 56
6.2. Simple Capturing Video from A Webcam with OpenCV 65
6.3. Simple Controlling PTZ Camera through RS232 70
Ref- 1 Foresti, G.L.; Micheloni, C.; Snidaro, L.; Remagnino, P.; Ellis, T.; “Active video-based surveillance system: the low-level image and video processing techniques needed for implementation”, Signal Processing Magazine, IEEE, Volume 22, Issue 2, Mar 2005 Page(s):25 – 37
Ref- 2 Valera, M.; Velastin, S.A.; “Intelligent distributed surveillance systems: a review”, Vision, Image and Signal Processing, IEE Proceedings-, Volume 152, Issue 2, 8 April 2005 Page(s):192 – 204
Ref- 3 R.T. Collins, A.J. Lipton, H. Fujiyoshi, and T. Kanade, “A system for video surveillance and monitoring”, Proc. IEEE, vol. 89, no. 10, pp. 1456–1477, Oct. 2001.
Ref- 4 C. Stauffer and E. Grimson, “Learning patterns of activity using real-time tracking,’’ IEEE Trans. Pattern Anal. Machine Intell., vol. 22, no. 8, pp. 747–757, 2000.
Ref- 5 D. Comaniciu, V. Ramesh, P. Meer, “Real-time tracking of non-rigid objects using mean shift,” IEEE International Conference on Pattern Recognition, vol. 2, pp. 142-149, 13-15 June, 2000.
Ref- 6 K. Nummiaro, E. Koller-Meier, and L. Van Gool, “A color based particle filter,” in First International Workshop on Generative-Model-Based Vision, A.E.C. Pece, Ed., 2002.
Ref- 7 R. Hartley and A. Zisserman, “Multiple View Geometry in computer vision”,2nd edition, Cambridge University Press, 2004.
Ref- 8 O. Faugeras and Q. Luong, “The Geometry of Multiple Images”,1st edition, The MIT Press, 2001.
Ref- 9 R. C. Gonzalez and R. E. Woods, “Digital Image Processing”,2nd edition, Pearson Education International, 2002.
Ref- 10 P. Rosin. Thresholding for change detection. In Proceedings of IEEE Int’l Conf. on Computer Vision, pages 274–279, 1998.
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