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DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 洪一平 | |
dc.contributor.author | Cheng-Chih Tsai | en |
dc.contributor.author | 蔡承志 | zh_TW |
dc.date.accessioned | 2021-06-14T16:57:50Z | - |
dc.date.available | 2013-08-19 | |
dc.date.copyright | 2011-08-19 | |
dc.date.issued | 2011 | |
dc.date.submitted | 2011-08-12 | |
dc.identifier.citation | [1] B. Hongliang and L. Changping, “A hybrid license plate extraction method based on edge statistics and morphology,” in Proc. ICPR, 2004, pp. 831–834.
[2] D. Zheng, Y. Zhao, and J. Wang, “An efficient method of license plate location,” Pattern Recognit. Lett., vol. 26, no. 15, pp. 2431–2438, Nov. 2005. [3] A. Broumandnia and M. Fathy, “Application of pattern recognition for Farsi license plate recognition,” in Proc. Int. Conf. GVIP, Cairo, Egypt, 2005. [4] C. Anagnostopoulos, I. Anagnostopoulos, E. Kayafas, and V. Loumos, “A license plate recognition system for intelligent transportation system applications,” IEEE Trans. Intell. Transp. Syst., vol. 7, no. 3, pp. 377–392, Sep. 2006. [5] C. Anagnostopoulos, I. Anagnostopoulos, I. Psoroulas, and V. Loumos, “License plate recognition from still images and video sequences: Asurvey,” IEEE Transactions on Intelligent Transportation Systems, vol. 9, no. 3, pp. 377–391, 2008. [6] K. Deb, S. Kang, and K. Jo, “Statistical characteristics in HSI color model and position histogram based vehicle license plate detection,” Intell. Serv. Robotics 2, 173–186, 2009. [7] S. Nomura, K. Yamanaka, O. Katai, H. Kawakami, and T. Shiose,“A novel adaptive morphological approach for degraded character image segmentation,” Pattern Recognit., vol. 38, no. 11, pp. 1961–1975, Nov. 2005. [8] A. Capar and M. Gokmen, “Concurrent segmentation and recognition with shape-driven fast marching methods,” in Proc. 18th ICPR, Hong Kong, 2006, vol. 1, pp. 155–158. [9] X. Pan, X. Ye, and S. Zhang, “A hybrid method for robust car plate character recognition,” Eng. Appl. Artif. Intell., vol. 18, no. 8, pp. 963– 972, Dec. 2005. [10] P. Comelli, P. Ferragina,M. N. Granieri, and F. Stabile, “Optical recognition of motor vehicle license plates,” IEEE Trans. Veh. Technol., vol. 44, no. 4, pp. 790–799, Nov. 1995. [11] Han, C.-C., Hsieh, C.-T., Chen, Y.-N., Ho, G.-F., Fan, K.-C., and Tsai, C.-L., “License plate detection and recognition using a dual-camera module in a large space,” Security Technology, 2007 41st Annual IEEE International Carnahan Conference on , 307–312, Oct. 2007. [12] C. Stauffer and W.E.L. Grimson, 'Adaptive Background Mixture Models for Real-Time Tracking,' Proc. Computer Vision and Pattern Recognition 1999 (CVPR '99), June 1999. [13] G. Welch and G. Bishop, “An introduction to the Kalman filter,” Dept. Comput. Sci., Univ. North Carolina, Chapel Hill, Tech. Rep. TR95041, 2000. [14] N. Funk. A study of the Kalman filter applied to visual tracking. Technical report, University of Alberta, 2003. [15] P. Shivakumara, T.Q. Phan, and C.L. Tan, “A Laplacian Approach to Multi-Oriented Text Detection in Video,” IEEE Trans. PATTERN ANALYSIS AND MACHINE INTELLIGENCE, vol. 33, no. 2, pp. 412-419, Feb. 2011. [16] E.K. Wong and M. Chen, “A New Robust Algorithm for Video Text Extraction,” Pattern Recognition, vol. 36, pp. 1397-1406, 2003. [17] N. Otsu, “A threshold selection method from gray-level histograms,” IEEE Trans. Biomed. Eng., vol. BME-9, pp. 63–66, 1979. [18] I.T. Jolliffe, Principal Component Analysis. New York: Springer-Verlag, 1986. [19] B. E. Boser, I. Guyon, and V. Vapnik, “A training algorithm for optimal margin classifiers,” In Proceedings of the Fifth Annual Workshop on Computational Learning Theory, pp. 144-152, ACM Press, 1992. [20] C. Chang and C. Lin, LIBSVM: A Library for Support Vector Machines, 2001, available at http://www.csie.ntu.edu.tw/cjlin/libsvm. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/40731 | - |
dc.description.abstract | 近年來,智慧型監控系統在日常生活中的應用越來越廣泛。在本論文裡,我們提出一套結合主從式智慧型監控與車牌辨識的系統。此系統硬體由兩支攝影機組成,其中一台為固定式場域監控攝影機,一台為高速球型攝影機。此系統可以監控廣泛區域,並同時取得監控場景中車輛的高解析度清晰影像,以進行車牌辨識。為了達成上述功能,我們必須在固定式場域監控攝影機畫面裡取出車輛的影像,預測其可能移動的路徑,並控制高速球型攝影機進行旋轉追蹤。此外,由於我們所監控的場景為一廣泛區域,車輛會由不同角度及路徑經過。可是傳統的車牌辨識方法僅能針對正面車牌影像進行偵測與辨識。因此,我們提出一可於不同的距離與角度進行準確的車牌辨識方法。實驗結果顯示,所提出的方法可自動估測車輛行進軌跡並取得高解析度車牌影像,且能對不同視角之車牌影像進行偵測與辨識。 | zh_TW |
dc.description.abstract | In the last few years, intelligent visual surveillance system plays an important role in our daily life. In this thesis, we combine a master-slave dual-camera system with a novel license plate recognition technique. Our system is consisted of two cameras, one is a wide-angle, fixed camera, and the other is a speed dome camera. The system is able to monitor a wide area with wide-angle camera and grabs high-resolution images with the speed dome camera to process license plate recognition. In order to achieve the functions mentioned above, we have to detect the vehicle from image sequences of wide angle fixed camera first, predict the position of the vehicle, and then control the speed dome camera to track it. In addition, since the monitored area is large, the view angle to the license plate may be arbitrary. Compare to traditional license plate recognition methods, which can only deal with the almost frontal view cases, here, we propose a novel method, which can recognize license plates even with different distances and arbitrary view-angles. The experimental results show our method can detect and predict the vehicle, and further recognize the inclined license plate well. | en |
dc.description.provenance | Made available in DSpace on 2021-06-14T16:57:50Z (GMT). No. of bitstreams: 1 ntu-100-R98922064-1.pdf: 3580296 bytes, checksum: fddd96749ab4556d6eb45b53bb631926 (MD5) Previous issue date: 2011 | en |
dc.description.tableofcontents | 誌謝 i
摘要 ii Abstract iii Contents iv List of Figures vi Chapter 1 Introduction 1 Chapter 2 Related Work 4 2.1 License Plate Recognition 4 2.2 Dual-Camera System 8 Chapter 3 System Overview 10 Chapter 4 The Dual Camera System for Vehicle Tracking 12 4.1 Simplified Vehicle Detection 13 4.1.1 Background Subtraction 13 4.1.2 Vehicle Extraction via Adaptive Learning 15 4.2 Vehicle Tracking 16 Chapter 5 License Plate Recognition 20 5.1 License Plate Detection 21 5.1.1 Text Detection 21 5.1.2 Candidate Image Refining 26 5.1.3 Non-Plate Region Elimination 27 5.2 Character Segmentation 29 5.2.1 Plate Warping 29 5.2.2 Connected Component Analysis 32 5.3 Character Recognition 33 5.3.1 Feature Extraction 34 5.3.2 Classifier 35 Chapter 6 Experiments 37 6.1 License Plate Recognition Experiment 37 6.2 License Plate Recognition with a Dual-Camera System Experiment 39 Chapter 7 Conclusion 41 Bibliography 42 | |
dc.language.iso | en | |
dc.title | 使用雙攝影機系統於大範圍區域進行車牌辨識 | zh_TW |
dc.title | License Plate Recognition in a Large Area with a Dual-Camera System | en |
dc.type | Thesis | |
dc.date.schoolyear | 99-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 徐繼聖,李明穗,李秉翰 | |
dc.subject.keyword | 車牌辨識,影像監控,雙攝影機系統, | zh_TW |
dc.subject.keyword | license plate recognition,video surveillance,dual camera system, | en |
dc.relation.page | 44 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2011-08-12 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
顯示於系所單位: | 資訊工程學系 |
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