在今日的國防科技中，必須追蹤飛行器以評估飛行器的效能。而隨著航太科技的進步，飛行器的速度不斷的加快，傳統以人力追蹤拍攝飛行物的方式無法滿足現今的需求。有鑑於此，本論文提出了一套電腦輔助飛行物追蹤的技術。此技術可以針對移動式紅外線熱影像攝影機與移動式光學攝影機進行飛行物的追蹤拍攝。另外，此項技術不但可以用於飛行器追蹤上，也可用在飛行物的發射與運動軌道之分析、監視預警系統…。

　　本篇論文主要有兩個重點：一是飛行物的偵測，另一是飛行物的追蹤。

　　在偵測飛行物時，天候扮演著重要的角色。天空有時晴朗、有時有雲霧，這些因素都將會影響到偵測與追蹤飛行物的結果。因此本論文在偵測飛行物的步驟中，首先分析影像場景的雜亂度，將天空場景分為場景雜亂與場景不雜亂等兩種情況。在飛行物偵測的過程中，根據不同場景使用不同方式來擷取出飛行物影像。

　　在飛行物追蹤運用動態階層表示法的模型架構，以動態階層表示每個飛行物，並建立出位移、形狀與外觀等三個模型。而這些模型參數隨著時間的改變，動態的估計與更新。在估計位移模型參數的過程中，加入卡爾曼濾波器來預測飛行物在目前畫面的初始位置，並在初始位置上訂出一搜尋視窗以搜尋飛行物。經由實驗結果顯示出，本論文所提出方法能夠正確的偵測與追蹤飛行物。

　　In modern defensive techniques, it is an important task to detect and track a flying subject from the acquired image sequence. With the advance in aerospace industry, the speed of flying machine is getting faster. Therefore, the conventional method depended it on manual tracking can not satisfy the demands of on-line tracking. To cope with this problem, we propose a new computer aided tracking method which can track multiple flying targets in an image sequence acquired from an infrared or an optical camera. The proposed method in this thesis can be applied to flying target tracking and many other fields, such as launch and motion analysis, and surveillance system.

　　There are two main topics in the thesis. One is flying target detection, and the other is flying target tracking. However, weather conditions play an important role in flying target detection. The changes in weather condition may influence the complexity in the subsequent image analyses and tracking results. Therefore, it is necessary to evaluate the weather condition in the first step. The weather condition of image scene is defined either clutter or not clutter. According to the weather type, we then utilize different approach to extract the flying targets in the process of target detection.

　　In flying target tracking, we used the dynamic layer to represent the flying target and built the motion, shape and appearance models for each layer. In each iteration, the model parameters were estimated and updated dynamically. In tracking the object, we used the Kalman filter to estimate the central position of object on which the search window of interest is defined. In the experiment of real world image sequences, the proposed method can successfully detect and track the objects.

[1] M. Z. Tidrow and W. R. Dyer,“Infrared sensors for ballistic missile defense,”Infrared Physics & Technology, vol. 42, iss. 3-5, pp. 333-336,June. 2001.
[2] T. Kirubarajan, Y. Bar-Shalom, and Y. Wang,“Passive ranging of a low observable ballistic missile in a gravitational field,”IEEE Transactions on
Aerospace and Electronic Systems, vol. 37, no. 2, pp. 481-494, April 2001.
[3] G. M. Siouris, G. Chen, and J. Wang,“Tracking an incoming ballistic missile using an extended interval kalman filter,”IEEE Transactions on Aerospace and Electronic Systems, vol. 33, no. 1, pp. 232-240, Jan. 1997.
[4] T. L. Song, J. Y. Ahn, and T. Y. Um,“A passive tracking filter for missile capture,”IEEE Transactions on Aerospace and Electronic Systems, vol. 26, no.5, pp. 867-875, Sep. 1990.
[5] D. J. Difilippo and L. L. Campbell,“Design and implementation of a tracking algorithm for active missile approach warning systems,”In Proceedings of the Canadean Conference on Electrical and Computer Engineering, pp. 756-759, 1995.
[6] L. Li, W. Huang, I. Y. H. Gu and Q. Tian,“Statistical Modeling of Complex
Backgrounds for Foreground Object Detection,”IEEE Transactions on Image Processing, vol. 13, no. 11, Nov. 2004.
[7] M. Zaki, M. Y. El Nahas and M. Youssef,“EMBOT: An enhanced motion-based
object tracker,”The Journal of Systems and Software, vol. 69, no. 1-2, pp.
149-158, Jan. 2004.
[8] D. Murray and A. Basu,“Motion Tracking with an Active Camera,”IEEE
Transactions on Pattern Analysis and Machine Intelligence, vol. 16, pp. 449-459, May. 1994.
[9] J. R. Bergen, P. Anandan, K. J. Hanna, and R. Hingorani,“Hiearchical
Model-Based Motion Estimation,”Proc. European Conf. Computer Vision, pp.
237-252, 1992.
[10] G. Rigoll, H. Breit and F. Wallhoff,“Robust tracking of persons in real-world scenarios using a statistical computer vision approach,”Image and Vision
Computing, vol. 22, no. 7, pp. 571-582. July 2004.
[11] H. Breit, G. Rigoll,“Improved Person Tracking Using a Combined Pseudo-2D-HMM and Kalman Filter Approach with Automatic Background State Adaptation,” Proc. IEEE International Conference on Image Processing(ICIP), October 2001.
[12] S. Kuo, O. E. Agazzi,“Keyword spotting in poorly printed documents using
pseudo 2-D hidden Markov models,”IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 842-848, 1994.
[13] S. M. Maillet,“1D and Pseudo-2D Hidden Markov Models for Image Analysis.
Theoretical. Introduction,”Tech. Rep. RR-99-49 Part A, Institute EURECOM, Multimedia Communications, November. 30, 1999.
[14] L. R. Rabiner,“A tutorial on hidden Markov models and selected applications in speech recognition,”Proceedings of the IEEE, vol. 77, no. 2, pp 257–285, Feb. 1989.
[15] H. Tao, H. S. Sawhney and R. Kumar,“Object Tracking with Bayesian Estimation of Dynamic Layer Representations,”IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 1, pp. 75-89, Jan. 2002.
[16] J. Y. A. Wang and E. H. Adelson,“Layered Representation for Motion Analysis,”Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 361-366, 1993.
[17] S. Ayer and H. S. Sawhney,“Layered Representation of Motion Video Using
Robust Maximum-Likelihood Estimation of Mixture Models and MDL Encoding,”Proc. IEEE Int’l Conf. Computer Vision, pp. 777-784, 1995.
[18] N. Vasconcelos,“Emprical Bayesian EM-Based Motion Segmentation,”Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 527-532, 1997.
[19] A. P. Dempster, N. M. Laird, and D. B Rubin,“Maximum Likelihood from
Incomplete Data via the EM Algorithm,”Journal of the Royal Statistical Society, ser. B (Methodological) , vol. 39, no. 1, pp. 1-38, 1977.
[20] P. Rosin,“Thresholding for change detection,”Proc. IEEE Int. Conf. Computer Vision, pp. 274–279. Jan. 1998.
[21] G. Welch and G. Bishop,“An introduction to the kalman filter,”Technical
Report TR 95-041, University of North Carolina, Department of Computer Science, 1995.
[22] C. Zhu, W. S. Qi and W. Ser,“Predictive Fine Granularity Successive
Elimination for Fast Optimal Block-Matching Motion Estimation,”IEEE Transactions on Image Processing, vol. 14, no. 2, pp. 213-221, 2005.
[23] W. Li and E. Salari,“Successive elimination algorithm for motion estimation,”IEEE Transactions on Image Processing, vol. 4, no. 1, pp. 105-107, Jan.1995.
[24] M. Brnig and W. Niehsen,“Fast full-search block matching,”IEEE Trans.
Circuits Syst. Video Technol., vol. 11, no. 2, pp. 241–247, Feb. 2001.
[25] J. N. Kim, S. C. Byun, Y. H. Kim, and B. H. Ahn,“Fast full search motion
estimation algorithm using early detection of impossible candidate vectors,”IEEE Trans. Signal Process., vol. 50, no. 9, pp. 2355–2365, Sep. 2002.
[26] C. Zhu, W. S. Qi, and Wee Ser,“A new successive elimination algorithm for fast block matching in motion estimation,”ISCAS, vol.3, pp 733-736, 2004.
[27] X. Q. Gao, C. J. Duanmu, and C. R. Zou,“A multilevel successive elimination algorithm for block matching motion estimation,”IEEE Trans. Image Process., vol. 9, no. 3, pp. 501-504, Mar. 2000.
[28] M. Sonka, V. Hlavac, and R. Boyle,“Image Processing Analysis, and Machine
Vision,”Second edition, PWS Publishing, 1998.
[29] R. C. Gonzalez and R. E. Woods,“Digital Image Processing,”Second edition,Prentice Hall, 2002.
[30] L. G. Shapiro and G. C. Stockman,“Computer Vision,”Prentice Hall, 2001.
[31] M. Pharr, R. Fernando,“GPU Gems 2: Programming Techniques for High-Performance Graphics and General-Purpose Computation”, Addison-Wesley Publishing, 2005.
[32] R. J. Rost,“OpenGL Shading Language,”Addison Wesley, 2006.
[33] 楊英魁, 孫宗瀛, 鄭魁香,“模糊控制理論與技術,”全華圖書, 1996.
[34] 何宗穎, 翁子倫, 孫永年,“機動式光學追蹤系統電腦輔助追瞄軟體研製,”第14 屆國防科技學術研討會暨國防部軍備局中山科學研究院、國科會94年度國防科技學術合作計畫成果發表會會議, 2005.