本論文的研究目的在於利用FPGA設計與實現即時的視覺目標追蹤系統。在早期，移動物偵測皆是以電腦為平台，搭配影像處理方法所實現的目標追蹤系統，故必需先將影像傳送到電腦端運算過後再針對感興趣的物件下達控制命令。本研究以XILINX-XC3S400 40萬Gate count之FPGA為演算法設計平台，結合CMOS Sensor PAS6311影像擷取設備以及步進馬達完成即時移動物追蹤系統，其演算法主要是以適應性背景相減法為基礎，優點在於能適應背景中微小的變化，如樹葉晃動等，並利用高斯標準差做為適應性的臨界值以有效的分離移動物。為了實現真正的即時偵測，本系統演算法的運算皆是由FPGA中邏輯閘電路所構成，利用管線式設計以快速取得高斯標準差，並提出雙計數器設計方法改良形態學斷開的遮罩運算所造成的延遲，以及整合SRAM記憶體裝置與步進馬達控制，故本系統可在每張影像輸出完後即時偵測出移動物並予以追蹤，確保目標物保持在可追蹤範圍內。從移動物與鏡頭的相對位置可呈現出本系統即時追蹤的效果。

The research purpose of this thesis is to design and implement a real-time visual target tracking system via FPGA technique. In the past researches, target tracking system was mainly operated on PC-based platform, thus it must first transmit the image to the computer terminal to operate and then give control command to interested object. This study adopts FPGA XILINX-XC3S400 which has 400,000 gate counts, CMOS Sensor PAS6311 image capture device and Stepper Motor to complete the real-time target tracking system. The algorithm is mainly based on adaptive background subtraction. Advantage lies in its ability to adapt to small changes in the background, such as swaying leaves and use Gaussian standard deviation as adaptive threshold to effectively separate moving object. In order to achieve true real-time detection, the operations of this system algorithm are all constituted by logic gates of FPGA. Using pipe-line design to quickly obtain Gaussian standard deviation, and proposed the design method of the bi-counters to improve the delay caused by mask operation of Morphological Opening. Furthermore, SRAM memory device and Stepper Motor control are integrated to achieve our design. Hence, this system can immediately detect and to track moving object after each image was output, ensure that the target can be maintain in the traceability scope. From the relative position of moving object and camera lens can display the effectiveness of our real-time tracking system.

[1] C. Cedras, and M. Shah, “Motion-based recognition: A survey,” Image and Vision Computing, Vol. 13, pp. 129-154, 1995.
[2] A. Baumberg, and D. C. Hogg, “Learning deformable models for tracking the human body,” in Motion-Based Recognition, Eds. Norwell, MA: Kluwer, 1996, pp. 39–60.
[3] N. Peterfreund, “The Velocity Snake,” Proc. IEEE Nonrigid and Articulated Motion Workshop, Virgin Islands, 1997.
[4] N. Peterfreund, “Robust tracking of position and velocity with Kalman snakes,” IEEE Trans. Pattern Anal. Machine Intell., vol. 22, pp. 564–569, June 2000.
[5] R. Yang, and Z. Zhang, “Model-Based Head Pose Tracking With Stereovision,” IEEE International Conference on Automatic and Gesture Recognition, Vol. 20-21, pp. 242- 247, 2002.
[6] D. Comaniciu, V. Ramesh, and P. Meer , “Kernel-based object tracking”, Real-Time Vision & Modeling Dept., Siemens Corporate Res., Princeton, NJ, USA.
[7] D.-S. Jang, and H.-I. Choi, “Active models for tracking moving objects, ”Pattern Recognit, vol. 33, no. 7, pp. 1135–1146, 2000.
[8] R. E. Kalman, “A new approach to linear filtering and prediction problems,” Transactions of the ASME - Journal of Basic Engineering Vol. 82: pp. 35-45 (1960).
[9] C. Kim, and J. N. Hwang, “A fast and robust moving object segmentation in video sequences,” in Proc. of the IEEE International Conference on Image Processing(ICIP’99), vol.2, Kobe, Japan, pp. 131-134, Oct. 1999.
[10] C. Kim, and J. N. Hwang, “Fast and automatic video object segmentation and tracking for content-based applications,” IEEE Transactions Circuits and System for Video Technology, vol.12, pp.122-129, Feb. 2002.
[11] C. R. Wren, A. Azarbayejani, T. Darrell, and A. P. Pentland, “Pfinder: real-time tracking of the human body,” IEEE Trans. Pattern Anal. Machine Intell., vol. 19, pp. 780–785, July 1997.
[12] S. McKenna, S. Jabri, Z. Duric, A. Rosenfeld, and H. Wechsler, “Tracking groups of people,”Comput. Vis. Image Understanding, vol. 80, no. 1, pp. 42–56, 2000.
[13] B. K. P. Horn, and B. G. Schunck, “Determining optical flow,” Artificial Intelligence, vol.17, pp.185-203, 1981.
[14] J. Barron, D. Fleet, and S. Beauchemin, “Performance of Optical Flow Techniques,” International Journal of Computer Vision, vol.12, no.1, pp.44-77, Jan. 1994.
[15] A. M. Tekalp, “Digital video processing,” Prentice Hall PTR, 1995.
[16] D. Murray, and A. Basu, “Motion Tracking with an Active Camera,” IEEE Trans. On Pattern Analysis and Machine Intelligence, Vol. 16, No. 5, pp. 449- 459, 1994.
[17] R. T. Collins, A. J. Lipton, T. Kanade, H. Fujiyoshi, D. Duggins, Y. Tsin, D. Tolliver, N. Enomoto, O. Hasegawa, P. Burt, and L. Wixson, “A system for surveillance and monitoring,” The Robotics Institute, Carnegie Mellon University, Pittsburgh PA, Tech. Rep. CMU-RI-TR-00-12, 2000.
[18] C. Ridder, O. Munkelt, and H. Kirchner. “Adaptive Background Estimation and Foreground Detection using Kalman-Filtering,” Proceedings of International Conference on recent Advances in Mechatronics, ICRAM
[19] C. Stauffer, and W. E. L. Grimson, “Adaptive background mixture models for real-time tracking,” IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 2, pp. 246-252, 1999.
[20] P. KaewTraKulPong, and R. Bowden, “An improved background mixture model for real-time tracking with shadow detection,” Proc. 2nd European Workshop on Advanced Video Based Surveillance Systems, Vol. 25, 2001.
[21] I. Haritaoglu, D. Harwood, and L. S. Davis, “: Real-time surveillance of people and their activities,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 22, No. 8, pp. 809-830, 2000.
[22] 王俊凱, ”基於改良式適應性背景相減法與多重影像特徵比對法之多功能即時視覺追蹤系統之設計與實現” ,國立成功大學電機工程學系, 碩士論文, 2004.
[23] 賴俊良, “移動目標物視覺偵測與追蹤研究,” 國立成功大學電機工程學系, 碩士論文, 2006.
[24] 王元凱, 胡永祥, “智慧型視訊分析於行動網路視覺監控之應用,” 輔仁大學電子工程學系.
[25] 黃俊霖, “以CamShift演算法為基礎之目標物即時影像追蹤系統設計與實現,” 國立成功大學工程科學系, 碩士論文, 2009.
[26] “Universal Serial Bus Specification,” USB Implementers Forum, Reversion 2.0 2000.
[27] “EZ-USB FX2 Technical Reference Manual,” Cypress Semiconductor, Version 2.2, 2001.
[28] “CY7C68013 EZ-USB FX2 USB Microcontroller High-Speed USB Peripheral Controller,” Cypress Semiconductor, 2002.
[29] “USB device with serial interface,” Philips Semiconductor, 1998.
[30] “PAS6311 Application Note,” PixArt Imaging, Inc, 2007.
[31] “PAS6311LT Specification,” PixArt Imaging, Inc, 2007.
[32] ”Spartan-3 FPGA Family: Complete Data Sheet,” Xilinx, Inc, 2004.
[33] “Spartan-3 Generation FPGA User Guide,” Xilinx, Inc, 2009.
[34] Rafael C. Gonzalez, Richard E. Woods, “DIGITAL IMAGE PROCESSING,” published by Pearson Education, Inc, publishing as Prentice Hall, 2002.
[35] 鄭信源, 硬體描述語言數位電路, 儒林圖書, 2004.