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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/38000完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 傅楸善 | |
| dc.contributor.author | Shin-Ming Chen | en |
| dc.contributor.author | 陳信銘 | zh_TW |
| dc.date.accessioned | 2021-06-13T15:55:46Z | - |
| dc.date.available | 2011-06-24 | |
| dc.date.copyright | 2008-06-24 | |
| dc.date.issued | 2008 | |
| dc.date.submitted | 2008-06-14 | |
| dc.identifier.citation | [1] E. Atkociunas, R. Blake, A. Juozapavicius, and M. Kazimianec, “Image Processing in Road Traffic Analysis, ” Nonlinear Analysis: Modelling and Control, Vol. 10, No. 4, pp. 315–332, 2005.
[2] B. Coifman, D. Beymer, P. McLauchlan, and J. Malik, “A Real-Time Computer Vision System for Vehicle Tracking and Traffic Surveillance,” Transportation Research Record Part C: Emerging Technologies, Vol. 6, Issue 4, pp. 271-288, 1998. [3] M. Han, W. X. H. Tao, and Y. H. Gong, “An Algorithm for Multiple Object Trajectory Tracking,” Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Cupertino, CA, Vol. I, pp. 864-871, 2004. [4] R. A. Hicks and R. Bajcsy, “Reflective Surfaces as Computational Sensors,” Image and Vision Computing, Vol. 19, Issue 11, pp. 773-777, 2001. [5] Intel Corporation, “Open Source Computer Vision. Library: Reference Manual”, 2001. [6] G. Y. Jiang, S. N. Wang, M. Y., T. YU. Choi, Y. D. Kim, “New Method of Vision- Based Vehicle Detection and Tracking in Complicated Background,” Proceedings of TENCON: IEEE Region 10 Conference, Chiang Mai, Thailand, Vol. 1, pp. 387- 390, 2004. [7] N. K. Kanhere, S. J. Pundlik, and S. T. Birchfield, “Vehicle Segmentation and Tracking from a Low-Angle Off-Axis Camera,” Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, San Diego, California, Vol. 2, pp. 1152- 1157, 2005. [8] D. Koler, J. Weber, and J. Malik, “Robust Multiple Car Tracking with Occlusion Reasoning,” eScholarship Repository, University of California. http://repositories.cdlib.org/its/path/papers/UCB-ITS-PWP-94-1, 1994. [9] Z. F. Liu and Z. S. You, “A Real-Time Vision-based Vehicle Tracking and Traffic Surveillance,” Proceedings of ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, Qingdao, China, pp. 174-179, 2007. [10] J. G. Lou, H. Yang, W. M. Hu, and T. N. Tan, “Visual Vehicle Tracking Using an Improved EKF,” Proceedings of Asian Conference on Computer Vision, Melbourne, Australia, pp. 23-25, 2002. [11] D. Scaramuzza and R. Siegwart, “A Practical Toolbox for Calibrating Omnidirectional Cameras,” Vision Systems: Applications, ISBN 978-3-902613-01-1 Ed. G. Obinata and A. Dutta, pp. 608, I-Tech, Vienna, Austria, 2007. [12] J. M. Wang, C. T. Tsai, C. Shen, and S. W. Chen, “Omni-Directional Camera Networks and Data Fusion for Vehicle Tracking in an Indoor Parking Lot,” Proceedings of the IEEE International Conference on Video and Signal Based Surveillance, Washington, DC, pp. 45-50, 2006. [13] S. Thrun, W. Burgard, and D. Fox, Probabilistic Robotics, MIT Press, Boston, pp. 39-64, 2005. [14] Y. Shan, F. Yang, and R. S. Wang, “Color Space Selection for Moving Shadow Elimination,” Proceedings of International Conference on Image and Graphics, Washington, DC, pp. 496-501, 2007. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/38000 | - |
| dc.description.abstract | 基於視覺的交通監控系統是電腦視覺的一項重要的研究，一般的交通資訊如車流量、車流速、壅塞狀況偵測、及壅塞長度預估等，有了正確的交通量分析才能做正確的交通指揮，都市交通規劃。在一般的天橋下裝上全方位照相機可以得到一個較為廣域的道路影像，將所得影像中的連續影格中利用各種電腦視覺的技術找到背景和前景，再分別將前景做車流量計算和對象追蹤，本文所提出的多輛車跟蹤的架構可以找出車輛，並且修正車子行經的軌道。根據我們的實驗性結果, 我們的方法可有效率地分析十字路口中行經公車、汽車及摩托車。 | zh_TW |
| dc.description.abstract | A vision-based system for vehicle detection and tracking in video streams is an important research in computer vision. It also plays an important role in ITS (Intelligent Transportation Systems). Mounting high omni-directional camera allows the omni-directional camera to cover a wider area. Motion detection aims to capture the changed region from the omni-directional video image sequences and build a background image. In this paper, a multiple-vehicle counting and tracking framework for intersection traffic surveillance is proposed. In order to accurately locate vehicles, we use Kalman filter to correct vehicle trajectories. According to our experimental results, our approaches can analyze the vehicles at intersection efficiently. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-13T15:55:46Z (GMT). No. of bitstreams: 1 ntu-97-R95922159-1.pdf: 1419033 bytes, checksum: c861a7cb36944cc295072b5bf05f9c7f (MD5) Previous issue date: 2008 | en |
| dc.description.tableofcontents | 誌 謝 i
摘 要 iii Abstract iv Figure Content vi Table Content ix Chapter 1 Introduction 1 1.1 Calibration and Dewarping 3 1.2 Background Model 5 1.3 Temporal Differencing 6 1.4 Combination Method 7 Chapter 2 Vehicle Detection 10 2.1 Shadow Removal 10 2.2 Motion Segmentation 11 Chapter 3 Vehicle Tracking 13 3.1 Kalman Filter 14 3.2 Tracking Model 16 3.3 Multiple Vehicle Tracking 18 3.4 Multiple-Vehicle Counting 21 Chapter 4 Experimental Results 23 4.1 Vehicle Classification 23 4.2 Precision and Recall 33 Chapter 5 Conclusion and Future Work 37 5.1 Conclusion 37 5.2 Future Work 40 Reference 41 | |
| dc.language.iso | en | |
| dc.subject | 交通監控 | zh_TW |
| dc.subject | 電腦視覺 | zh_TW |
| dc.subject | 車輛追蹤 | zh_TW |
| dc.subject | vehicle tracking | en |
| dc.subject | traffic surveillance | en |
| dc.subject | computer vision | en |
| dc.title | 全方位相機在交通路口車流量監控之應用 | zh_TW |
| dc.title | Omni-Directional Camera for Traffic Surveillance | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 96-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 黃英傑,李文豪,蔡育良 | |
| dc.subject.keyword | 電腦視覺,交通監控,車輛追蹤, | zh_TW |
| dc.subject.keyword | computer vision,traffic surveillance,vehicle tracking, | en |
| dc.relation.page | 41 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2008-06-15 | |
| 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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| ntu-97-1.pdf 未授權公開取用 | 1.39 MB | Adobe PDF |
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