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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 鄭士康(Shyh-Kang Jeng) | |
| dc.contributor.author | Po-Shao Lin | en |
| dc.contributor.author | 林柏劭 | zh_TW |
| dc.date.accessioned | 2021-06-13T02:09:09Z | - |
| dc.date.available | 2007-07-16 | |
| dc.date.copyright | 2007-07-16 | |
| dc.date.issued | 2007 | |
| dc.date.submitted | 2007-06-27 | |
| dc.identifier.citation | [1] D. Beymer, P. McLauchlan, B. Coifman and J. Malik, A Real-time Computer Vision System for Measuring Traffic Parameters, Computer Vision and Pattern Recognition (1997) 495-501.
[2] M. Grei_enhagen, V. Ramesh, D. Comaniciu and H. Niemann, Statistical Modeling and Performance Characterization of a Real-Time Dual Camera Surveillance System, Computer Vision and Pattern Recognition (2000) 335-342. [3] J. Segen and S. Pingali, A Camera-Based System for Tracking People in Real Time, International Conference on Pattern Recognition (1996) 63-67. [4] O. Javed and M. Shah, “Tracking and object classification for automated surveillance,” in Proc. European Conf. Computer Vision, vol. 4, 2002, pp. 343–357. [5] A survey on visual surveillance of object motion and behaviors. IEEE Trans. on Systems, Man, and Cybernetics - Part C, 34(3):334--352, August 2004.W. Hu, T. Tan, L. Wang, and S. Maybank. [6] S. L. Dockstader and A. M. Tekalp, “Multiple camera tracking of interacting and occluded human motion,” Proc. IEEE, vol. 89, pp.1441–1455, Oct. 2001. [7] R. T. Collins, A. J. Lipton, H. Fujiyoshi, and T. Kanade, “Algorithms for cooperative multi-sensor surveillance,” Proc. IEEE, vol. 89, pp. 1456–1477, Oct. 2001. [8] L. Li, W. M. Huang, I. Y. H. Gu, Q. Tian, “Statistical Modeling of Complex Backgrounds for Foreground Object Detection,” IEEE Transactions on Image Processing, vol. 13, No. 11, Nov. 2004. [9] Andrew H, S.Lai, Nelson. H, C. Yung. A fast and accurate scoreboard algorithm for estimating stationary backgrounds in an image sequence. Proceedings of IEEE International Symposium on Circuits and Systems, 1998:241∼244. [10] 王俊明，陳世旺，「漸進式背景影像建構」，師大學報：數理與科技類，民國91年。 [11] D.S. Lee, J.J. Hull and B. Erol, “A Bayesian Framework for Gaussian Mixture Background Modeling,” IEEE International Conference on Image Processing, Vol. 2, Barcelona, Spain, pp. 973-976, Sep. 2003 [12] Gonzalez, Rafael C., and Richard. E. Woods, Digital Image Processing, 2nd edition, Addison-Wesley, 1993. [13] H. P. Moravec, “Toward Automatic Visual Obstacle Avoidance,” Proc. of 5th International Joint Conference on Artifitial Intelligence, pp. 584, August 1977. [14] C. Harris and M.J. Stephens. A combined corner and edge detector. In Alvey Vision Conference, pages 147–152, 1988. [15] D. Comaniciu, P. Meer, “Mean Shift Analysis and Applications,' IEEE Int'l Conf. Comp. Vis., Kerkyra, Greece, 1197{1203, 1999. [16] Nummiaro K, Merier E K, Gool L V. An adaptive color-based particle filter[J]. Image and Vision Computing, 2003, 21(1): 99∼110 [17] L. Devroye, L. Gyorfi and G. Lugosi, “A Probabilistic Theory of PatternRecognition,” Applications of Mathematics. Springer-Verlag Inc., Vol. 31,pp. 25-32, 1996. [18] D. Comaniciu, V. Ramesh and P. Meer, Real-Time Tracking of Non-Rigid Objects using Mean Shift, Computer Vision and Pattern Recognition (2000) 142-149. [19] Erik Cuevas, Daniel Zaldivar, and Raul Rojas, Particle filter in vision tracking, 2005. [20] Texas Instruments,Inc., “TMS320DM642 Data Sheet”. June 2004 [21] Texas Instruments,Inc., “TMS320DM642 Technical Overview”. September 2002 [22] Texas Instruments,Inc., ”TMS320 C6000 CPU and Instruction Set Reference Guide”. [23] Texas Instruments,Inc.,“TMS320C64x DSP Two-Level Internal Memory Reference Guide”. [24] Texas Instruments,Inc., “The TMS320DM642 Video Port Mini-Driver” August 2003. [25] Texas Instruments,Inc., “TMS320C6000 Code Composer Studio Tutorial” February 2000. [26] Texas Instruments,Inc., “The TMS320C6000 DSP/BIOS User’s Guide” May 2000. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/30572 | - |
| dc.description.abstract | 近年來由於安全監視系統日益普及，因此動態偵測與物體追蹤被廣泛的應用在各處，例如商業大樓、便利商店、停車場等。它們可取代一些無聊且費時的工作，避免因人的疲倦所帶來的疏失；當偵測出緊急狀況時，也能即時回報，因此能大幅降低系統的成本。
本論文期望能透過兩台攝影機對經過畫面的物體作偵測與追蹤，並將追蹤的結果用雙眼視覺的原理來測量距離，如此便能得到移動物體的空間座標，並將整個系統實做於TI的DM642開發板上。本論文的物體偵測採用漸進方式建立背景，進而偵測出前景物體；而追蹤部分則先將偵測出的物體轉成色彩直方圖，再使用Particle Filter來追蹤物體的位置，最後找出兩攝影機物體影像的相似點來測距。 | zh_TW |
| dc.description.abstract | In recent years, with the population of surveillance system, motion detection and tracking objects are widely used in various places, such as commercial buildings, convenience stores, parking lots, and so on. It can replace some boring and time-consuming work. It can also avoid some mistakes due to our fatigue. Furthermore, when detecting emergency situations, the system can send messages immediately so that it can substantially reduce the system cost.
We expect that the system can detect and track objects which pass the scene using two cameras. Then we can use stereo vision to measure the distance according to the two tracking results. By doing so, we can get the space coordinates of moving objects and we can implement this system on TI’s DM642. For object detection we establish background by adopting progressive background, and detect the foreground. We also transfer the detected objects to color histogram when tracking. Then we use Particle Filter to track the position of objects. Finally, we find the likely points of two objects’ image to measure distance. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-13T02:09:09Z (GMT). No. of bitstreams: 1 ntu-96-J94921013-1.pdf: 1535039 bytes, checksum: 0bdf97499d41d818f9519a3e5de5d634 (MD5) Previous issue date: 2007 | en |
| dc.description.tableofcontents | 致謝 i
中文摘要 ii 英文摘要 iii 圖目錄 vii 第一章 緒論 1 1.1 研究背景與動機 1 1.2 文獻回顧 1 1.3 章節概述 2 第二章 物體偵測 4 2.1 背景建構 5 2.1.1 中位數背景建立 6 2.1.2 漸進式背景建立 6 2.2 分割物體 9 2.3 Harris特徵點擷取 11 第三章 物體追蹤 16 3.1 物體追蹤 16 3.2 描述物體 16 3.2.1 色彩密度函數 16 3.2.2 Bhattacharyya係數 18 3.2.3 核心函數(Kernel Function) 21 3.3 Particle Filter 23 3.3.1 Particle Filter模型 23 3.3.2 Particle Filter參數 24 3.3.3 Particle Filter流程 28 3.4 適應性Particle Filter 28 第四章 TI DM642 DSP簡介 31 4.1 DM642介紹 31 4.1.1 DM642 的硬體規格 31 4.1.2 DM642 CPU 單元 33 4.1.3 DM642的記憶體單元 33 4.1.4 視訊通道(Video Port) 35 4.1.5 網路介面 36 4.2 軟體開發流程 36 4.2.1 CCS發展環境 36 4.2.2 軟體發展流程 37 4.2.3 DSP/BIOS介紹 40 4.2.4 多執行緒(multi-thread) 41 第五章 實驗結果與討論 43 5.1 系統架構 43 5.2 追蹤結果與討論 45 5.3 雙眼視覺與對應點擷取結果 50 5.4 測距結果與討論 56 5.5 影像放大結果與討論 58 第六章 結論 59 參考文獻 60 | |
| dc.language.iso | zh-TW | |
| dc.title | 利用動態偵測與追蹤技術取得清晰影像之DSP系統實作 | zh_TW |
| dc.title | A DSP-Based System to Get Clear Image Using Motion Detection and Tracking Technique | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 95-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 歐陽明(Ming OuhYoung),葉丙成(Ping-Cheng Yeh) | |
| dc.subject.keyword | 影像追蹤,Particle Filter,動態偵測, | zh_TW |
| dc.subject.keyword | Image Tracking,Particle Filter,Motion Detection, | en |
| dc.relation.page | 62 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2007-06-29 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 電機工程學研究所 | zh_TW |
| 顯示於系所單位： | 電機工程學系 | |
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