| 研究生: |
楊義順 Yang, Yi-shun |
|---|---|
| 論文名稱: |
以移動向量理論為基礎之影像導引追蹤系統之研究 Study of Image Guiding Tracking System Based on Motion Vector |
| 指導教授: |
陳添智
Chen, Tien-chi |
| 學位類別: |
碩士 Master |
| 系所名稱: |
工學院 - 工程科學系 Department of Engineering Science |
| 論文出版年: | 2009 |
| 畢業學年度: | 97 |
| 語文別: | 英文 |
| 論文頁數: | 72 |
| 中文關鍵詞: | 移動向量 |
| 外文關鍵詞: | motion vector |
| 相關次數: | 點閱:32 下載:3 |
| 分享至: |
| 查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報 |
近年來,影像導引系統被廣泛的應用在各個領域,例如:行動載具之自動駕駛、飛彈追蹤系統、居家保全以及自走式機器人等等。影像導引系統主要是模擬人類的視覺,用來指引追蹤系統其目標物所在位置。本論文提出一個新穎的演算方法來達到“移動目標物追蹤”的目的。影像導引系統是先由CMOS sensor抓取灰階的圖像,再經本論文所提出的演算法將圖像經過二質化、邊緣偵測、動態估測等,最後將目標物移動後的位置偵測出來。影像處理的演算法結果用來提供驅動追蹤系統所需的資訊。在影像估測方面,提出一個簡單且有效率的演算法來使移動目標物的移動方向正確的被偵測出來,並且追蹤系統確實的移動到目標物移動後所停留的方向。本論文所提出的演算法只由單一顆DSP晶片來執行影像處理部份,搭配驅動的dsPIC晶片,建構出整個影像導引的追蹤系統。
為了證明本論文所提出的演算法的效能,利用靜態的實驗結果以及動態的實驗結果來證實,其靜態響應是展現影像處理演算法的效能,而動態響應是展現影像估測的效能。經由各種不同的實例所得到的實驗結果,來證明演算法其在現實環境的實用性。由實驗可知,本論文所提出的演算法結合簡單的硬體便能達到有效的移動目標物追蹤。
Recently, the image-guiding system is widely used in many applications, such as mobile vehicle auto-driving, missile tracking, security and autonomous mobile robot. The aim of image-guiding system is to imitate the human sense of vision. This thesis proposes a novel approach to perform the real world “moving target tracking ”task, which is able to guide a mobile vehicle. The image-guiding system uses the gray-level images from a CMOS sensor on the mobile vehicle to execute the image-processing. The scheme of image-processing provides the output information for drive the mobile vehicle. The motion estimation provides a simple and useful method between the moving target and desired output for tracking system. The overall algorithm is implemented by a single DSP chip combined with a dsPIC chip to achieve the real-time of target tracking.
To verify the effect of the proposed algorithm, experiments are executed to show the results of static response and dynamic response. The different experiments are adopted to prove the practicability in the real world. The experimental results demonstrate that the proposed algorithm combined with the hardware has good performance to achieve the target tracking task.
[1] J. Chen, W. E. Dixon, M. Dawson and M. McIntyre, “Homography-Based Visual Servo Tracking Control of a Wheeled Mobile Robot,” IEEE Trans. on Robotics, vol. 22, no. 2, pp. 406-415, April 2006.
[2] J. Stavnitzky and D. Capson, “Multiple Camera Model-Based 3-D Visual Servo,” IEEE Trans. on Robotics and Automation, vol. 16, no. 6, pp. 732-739, Dec. 2000.
[3] T. H. S. Li, S. J. Chang and W. Tong, “Fuzzy Target Tracking Control of Autonomous Mobile Robots by Using Infrared Sensors,” IEEE Trans. on Fuzzy System, vol. 12, no. 4, pp. 491-501, Aug. 2004.
[4] R. C. Luo and T. M. Chen, “Autonomous Mobile Target Tracking System Based on Grey-Fuzzy Control Algorithm,” IEEE Trans. on Industrial Electronics, vol. 47, no. 4, pp. 920-931, Aug. 2000.
[5] R. C. Luo, T. M. Chen and K. L. Su, “Target Tracking Using a Hierarchical Grey-Fuzzy Motion Decision-Making Method,” IEEE Trans. on Systems, Man and Cybernetics, vol. 31, no. 3, pp. 179-186, May 2001.
[6] J. R. Fienup, “Detecting Moving Targets in SAR Imagery by Focusing,” IEEE Trans. on Aerospace and Electronic Systems, vol. 37, no. 3, pp. 794-809, July 2001.
[7] G. Bebis, S. Louis, Y. Varol and A. Yfantis, “Genetic Object Recognition Using Combinations of Views,” IEEE Trans. on Evolutionary Computation, vol. 6, no. 2, pp. 132-146, April 2002.
[8] K. R. Namuduri, “Motion Estimation Using Spatio-Temporal Contextual Information,” IEEE Trans. on Circuits and Systems for Video Techanology, vol. 14, no. 8, pp.1111-1115, Aug. 2004.
[9] Y. L. Chan and W. C. Siu, “An Efficient Search Strategy for Block Motion Estimation Using Image Features,” IEEE Trans. on Image Processing, vol. 10, no. 8, pp. 1223-1238, Aug. 2001.
[10] S. Dasgupta and A. Banerjee, “Pattern Tracking and 3-D Motion Reconstruction of a Gigid Body From a 2-D Image Sequence,” IEEE Trans. on Systems, Man and Cybernetics, vol. 35, no. 1, pp. 116-125, Feb. 2005.
[11] H. Moon, R. Chellappa and A. Rosenfeld, “Optimal Edge-Based Shape Detection,” IEEE Trans. on Image Processing, vol. 11, no. 11, pp. 1209-1227, Nov. 2002.
[12] V. Boskovitz and H. Guterman, “An Adaptive Neuro-Fuzzy System for Automatic Image Segmentation and Edge Detection,” IEEE Trans. on Fuzzy Systems, vol. 10, no. 2, pp. 247-262, April 2002.
[13] D. Demigny, “On Optimal Linear Filtering for Edge Detection,” IEEE Trans. on Image Processiing, vol. 11, no. 7, pp. 728-737, July 2002.
[14] X. Wang, “Laplacian Operator-Based Edge Detectors,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 29, no. 5, pp. 886-890, May 2007.
[15] P. Bao, L. Zhang and X. L. Wu, “Canny Edge Detection Enhancement by Scale Multiplication,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 27, no. 9, pp. 1485-1490, Sept. 2005.
[16] J. Canny, “A Computational Approach to Edge Detection,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 8, no. 6, pp. 6679-698, Nov.1986.
[17] M. C. Shin, D. B. Goldgof, K. W. Bowyer and S. Nikiforou, “Comparison of Edge Detection Algorithms using a Structure from Motion Task,” IEEE Trans. on Systems, Man, and Cybernetics, vol. 31, no. 4, pp. 589-601, Aug.2001.
[18] M. Zhao, A. M. N. Fu and H. Yan, “A Technique of Three-Level Thresholding Based on Probability Partition and Fuzzy 3-Partition,” IEEE Trans. on Fuzzy Systems, vol. 9, no. 3, pp. 469-479, June 2001.
[19] N. D. Venkata and B. L. Evans, “Adaptive Threshold Modulation for Error Diffusion Halftoning,” IEEE Trans. on Image Processing, vol. 10, no. 1, pp. 104-116, Jan. 2001
[20] H. M. Lee, C. M. Chen, J. M. Chen and Y. L. Jou, “An Efficient Fuzzy Classifier with Feature Selection Based on Fuzzy Entropy,” IEEE Trans. on Systems, Man, and Cybernetics, vol. 31, no. 3, pp. 426-432, June 2001.
[21] P. K. Saha and J. K. Udupa, “Optimum Image Thresholding Via Class Uncertainty and Region Homogeneity,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 21, no. 7, pp. 689–706, 2001.
[22] A. Erturk and S. Erturk, “Two-Bit Transform for Binary Block Motion Estimation,” IEEE Trans. on Circuits and Systems for Video Technology, vol. 15, no. 7, pp. 938-946, July 2005.
[23] X. P. Zhang and M. D. Desai, “Segmentation of Bright Targets Using Wavelets and Adaptive Thresholding,” IEEE Trans. on Image Processing, vol. 10, no. 7, pp. 1020-1030, July 2001.
[24] J. H. Zheng and L. P. Chau, “A Motion Vector Recovery Algorithm for Digital Video Using Lagrange Interpolation,” IEEE Trans. on Broadcasting, vol. 49, no. 4, pp. 383-389, Dec. 2003.
[25] D. Wang, A. Vincent and P. Blanchfield, “Hybrid De-Interlacing Algorithm Based on Motion Vector Reliability,” IEEE Trans. on Circuits and Systems for Video Technology, vol. 15, no. 8, pp. 1019-1025, Aug. 2005.
[26] J. L. Chen and P. Y. Chen, “An Efficient Gray Search Algorithm for the Estimation of Motion Vectors,” IEEE Trans. on Systems, Man, and Cybernetics, vol. 31, no. 2, pp. 242-248, May 2001.
[27] F. Vella, A. Castorina, M. Mancuso and G. Messina, “Digital Image Stabilization by Adaptive Block Motion Vectors Filtering,” IEEE Trans. on Consumer Electronics, vol. 48, no. 3, pp. 796-801, Aug. 2002.