Responsive image
博碩士論文 etd-0625103-095754 詳細資訊
Title page for etd-0625103-095754
論文名稱
Title
光流技術在移動物體影像追尋上之應用
Visual Tracking for a Moving Object Using Optical Flow Technique
系所名稱
Department
畢業學年期
Year, semester
語文別
Language
學位類別
Degree
頁數
Number of pages
114
研究生
Author
指導教授
Advisor
召集委員
Convenor
口試委員
Advisory Committee
口試日期
Date of Exam
2003-06-12
繳交日期
Date of Submission
2003-06-25
關鍵字
Keywords
光流、SSD、影像追尋、運動能量法
SSD, Optical flow, Visual tracking, Motion energy methods
統計
Statistics
本論文已被瀏覽 5675 次,被下載 3814
The thesis/dissertation has been browsed 5675 times, has been downloaded 3814 times.
中文摘要
當一物體作連續變化的運動投影到平面上會產生一連串的影像,而攝影機與物體的運動會造成影像像素(pixel)的位移,然而此種位移的相對運動速度即稱為光流(Optical flow)。以光流做為計算基礎主要是因為其可以不需事前了解追蹤物體與當時環境,因此較適合應用於未知的環境進行追蹤。
經由研究證明,擷取整張影像作為求解光流的資訊並不一定能計算出正確的光流值;真正正確的光流往往出現在影像中有移動且是特徵的區域。因此本文先利用數位影像處理技術將兩張連續影像相減,找出真正發生位移的部分,接下來則是針對此部分作光流的演算。此方法不但可以提高追蹤速度也可降低計算出不正確光流值的機會,進而可以提高追蹤的準確度。


Abstract
When an object makes a motion of continuous variation, its projection on a plane brings a succession of image and the motion between the video camera and the object causes displacement of image pixels. The relative motion of the displacement is called optical flow. The advantage of using the optical flow approach is that it is not required to know characteristics of the object and the environment at that time. So this method is suitable for tracking problems in unknown environment.
It has been indicated that the optical flow based on the whole image cannot always be correct enough for control purpose where motion or feature occur. This thesis first uses digital image technique to subtract two continuous images, and extract the region where the motion actually occurs. Then, optical flow is calculated based on image information in this area. In this way, it cannot only raise the tracking speed, but also reduce the effect of the incorrect optical flow value. As a result, both tracking accuracy and speed can be greatly improved.


目次 Table of Contents
目錄
目錄……………………………………………………….………………I
圖索引………………………………………………………………….III
表索引………………………………………………………………...VIII
摘要……………………………………………………………………..IX
Abstract…………………………………………………………………X
第一章 緒論……………………………………………………………..1
1.1動機與目的………………………………………………….…...1
1.2文獻回顧………………………………………………………....3
1.3本文架構………………………………………………………....6
第二章 光流系統………………………………………………………..7
2.1光流及影像流之定義……………………………………….…...7
2.2以疊代法求解光流………………………………………….…...8
2.3以一階最小平方法求解光流……………………………….….13
2.4以二階最小平方法求解光流……………………………….….15
2.5以SSD求解光流…………………………………………….…21
2.6以改良式SSD三步法求解光流…………………………….…27
2.7光圈問題…………………………………………………….….30
第三章 攝影機座標與單位轉換………………………………………32
3.1攝影機模型………………………………………………….….32
3.2攝影機旋轉角度…………………………………………….….37
3.3單位轉換…………………………………………………….….38
第四章 擷取影像與提高精確度方法…………………………………43
4.1數位影像處理……………………………………………….….43
4.2運動能量法………………………………………………….….43
4.3邊緣檢測…………………………………………………….….48
4.4擷取影像流程……………………………………………….….49
4.5提高精度的方法…………………………………………….….51
第五章 控制架構與追尋實驗結果……………………………………57
5.1軟硬體設備概述…………………………………………….….57
5.2影像單步追尋實驗………………………………………….….60
5.3機台停頓時間分析………………………………………….….65
5.4連續影像追蹤……………………………………………….….69
5.5 SSD與改良式SSD三步法之比較……………………….…..101
第六章 結論與未來展望……………………………………………..106
參考文獻………………………………………………………………108
附錄 動態影像追尋…………………………………………………..113
參考文獻 References
參考文獻
[1] Adelson, E. H. and Bergen, J. R., “Spatiotemporal Energy Models for the Perception of Motion,” Journal of the Optical Society of America, A: Optics and Image Science, Vol. 2, pp. 284-299, 1985.
[2] Allen, P. K., Timcenko, A., Yoshimi, B. and Michelman, P., “Hand-Eye Coordination for Robotic Tracking and Grasping,” in Hashimoto, K. (Ed.), Visual Servoing, World Scientific Publishing Co. Pte. Ltd., pp. 33-69, 1993.
[3] Alparone, L., Barni, M., Bartolini, F. and Caldelli, R., “Regularization of Optic Flow Estimates by Means of Weighted Vector Median Filtering,” IEEE Transactions on Image Processing, Vol. 8, pp. 1462-1467, 1999.
[4] Anandan, P., “A Computational Framework and an Algorithm for Visual Motion,” International Journal of Computer Vision, Vol. 2, pp. 283-310, 1989.
[5] Bab-Hadiashar, A., and Suter, D., “Optical Flow Calculation Using Robust Statistics,” Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 988-993, 1997.
[6] Bainbrisge-Smith, A., and Lane, R.G., “Determining Optical flow Using a Differential Method,” Image and Vision Computing, Vol. 15, pp. 11-22, 1997.
[7] Jähne, B., Digital Image Processing, Springer-Verlag, Berlin Heidelberg, pp. 257, 1995.
[8] Chen, K. Y., Cheng, M.Y., and Tsai, M. C., “Design and Implementation of A Real-Time Pan-Tilt Visual Tracking System,” IEEE International Conference on Control Applications, pp. 736-741, 2002.
[9] Cretual, A., and Chaumette, F., “Image-based Visual Servoing by Integration of Dynamic Measurement”, Proceedings of the 1998 IEEE International Conference on Robotics and Automation, pp. 1994-2001, 1998.
[10] Feddema, J. T., Lee, C. S. G., and Mitchell, O. R., “Feature-based Visual Servoing of Robotic System,” in Hashimoto, K.(Ed.), Visual Servoing, World Scientific Publishing Co. Pte. Ltd., pp. 105-137, 1993.
[11] Fleet, D. J., and Jepson, A. D. “Computation of Component Image Velocity from Local Phase Information,” International Journal of Computer Vision, pp. 77-104, 1990.
[12] Hashimoto, K. and Kimura, H., “LQ Optimal and Nonlinear Approaches to Visual Servoing,” in Hashimoto, K. (Ed.), Visual Servoing, World Scientific Publishing Co. Pte. Ltd., pp. 165-197, 1993.
[13] Heeger, D. J., “Model for the Extraction of image Flow,” Journal of the Optical Society of America, Vol. 4, No.8 pp. 1455-1471, 1987.
[14] Heeger, D. J., “Optical Flow Using Spatiotemporal Filters,” International Journal of Computer Vision, pp. 279-302, 1988.
[15] Horn, B. K. P., and Schunck, B. G., “Determining Optical Flow,” Artificial Intelligence, Vol. 17, pp. 185-203, 1981.
[16] Hutchinson, S., Hager, G.D., and Corke, P.I., “A Tutorial on Visual Servo Control,” IEEE Trans on Robotics and Automation, Vol. 12, No. 5, pp. 651-670, 1996.
[17] Lai, S. H., and Vemuri, B. C., “Robust and Efficient Algorithms for Optical Flow Computation,” Proceeding of IEEE International Conference on Computer Vision, pp. 455-460, 1995.
[18] Lucas, B., and Kanade, T., “An Iterative Image Registration Technique with an Application to Stereo Vision,” Proc. DARPA Image Understanding Workshop, pp. 121-130, 1981.
[19] Nagel, H. H., “Displacement Vectors Derived from Second Order Intensity Variations in Image Sequences,” Computer Vision, Graphics and Image Processing, Vol. 21, pp. 85-117, 1983.
[20] Malls, E., Chaumette, F., and Boudet, S., “Positioning a Coarse-Calibrated Camera with Respect to An Unknown Object by 2D 1/2 Visual Servoing”, Proceedings of the 1998 IEEE International Conference on Robotics and Automation, pp. 1352-1359, 1998.
[21] Murray, D., and Basu, A., “Motion Tracking with an Active Camera,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.16, No. 5, pp. 449-459, 1994
[22] Park, W. T., and Hill, J., “Real Time Control of a Robot with a Mobile Camera,” Proc. 9th ISIR, Washington, D.C., pp. 233-246, 1979.
[23] Papanikolopoulos, N. P., Khosla, P. K., and Kanade, T., “Visual Tracking of a Moving Target by a Camera Mounted on A Robot:A combination of Control and Vision,” IEEE Trans. on Robotics and Automation, Vol. 9, No. 1, pp. 14-35, 1993.
[24] Simoncelli, E. P., Adelson, E. H., and Heeger, D. J., “Probability Distributions of Optical Flow,” Proceeding of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 310-315, 1991.
[25] Singh, A., Optical Flow Computation : A Unified Perspective, IEEE Computer Society Press, 1992.
[26] Sun, S., Haynor, D., and Kim, Y., “Motion Estimation Based on Optical Flow with Adaptive Gradients,” Proceeding of IEEE International Conference on Image Processing, Vol. 1, pp. 852-855, 2000.
[27] Uras, S., Girosi, F., Verri, A., and Torre, V., “A Computational Approach to Motion Perception,” Biol. Cybern., Vol. 60, pp. 79-87, 1988
[28] Wilson, W. J., ” Visual Servo Control of Robots Using Kalman Filter Estimates of Robot Pose Relative to Work-Pieces,” in Hashimoto, K(Ed.), Visual Servoing, World Scientific Publishing Co. Pte. Ltd., pp. 71-103, 1993.
[29] Wong, R. Y., and Hall, E. L., “Sequential Hierarchical Scene Matching,” IEEE Transaction on Computers, Vol. 27, No. 4, pp. 359-366, 1978.
[30] Ye, M., and Haralick, R. M., “Optical Flow From a Least-Trimmed Squares Based Adaptive Approach,” Proceedings of IEEE International Conference on Pattern Recognition, Vol. 3, pp. 1052-1055, 2000.
[31] 李慧婷,以特徵為基礎之光流計算方法,國立中山大學機械工程研究所碩士論文,中華民國八十八年六月。
[32] 何坤鑫,以光流為基礎之影像追尋,國立中山大學機械工程研究所碩士論文,中華民國九十年六月。
[33] 鄭銘揚,即時影像伺服追蹤控制,『影像伺服即時追蹤控制應用』專題研究產學交流成果發表會,中華民國九十年十二月十四日。
[34] 成灝然,統計學,三民書局,第一版,中華民國八十五年九月。

電子全文 Fulltext
本電子全文僅授權使用者為學術研究之目的,進行個人非營利性質之檢索、閱讀、列印。請遵守中華民國著作權法之相關規定,切勿任意重製、散佈、改作、轉貼、播送,以免觸法。
論文使用權限 Thesis access permission:校內外都一年後公開 withheld
開放時間 Available:
校內 Campus: 已公開 available
校外 Off-campus: 已公開 available


紙本論文 Printed copies
紙本論文的公開資訊在102學年度以後相對較為完整。如果需要查詢101學年度以前的紙本論文公開資訊,請聯繫圖資處紙本論文服務櫃台。如有不便之處敬請見諒。
開放時間 available 已公開 available

QR Code