臺灣博碩士論文加值系統

交通運輸為一國之命脈，其重要性有如血液循環系統之於人體。許多先進國家每年花費龐大的經費，來改善擴充他們的交通運輸系統。過去各國政府為了因應日益膨脹的運輸需求，而以不斷地擴充硬體設施（如道路，交通工具）的方式來增加運輸的量與能。只是前者的擴充速度總是趕不上後者的增加速度；尤其對於人稠地狹的區域，擴充硬體設備幾乎已是難以施展。於是如何在現有的架構下，以目前已知的科技，如電腦、網路、通訊、控制、以及先進管理等技術，來提升交通運輸的效率、安全與舒適，便成為一個較為務實的目標。
交通監控的目的乃在於蒐集重要路口及路段的交通資料，如車流量、車距、平均車速和加速度、車行方向和軌跡、以及路面佔有率等。這些數據傳回交通控制中心，可以協助交通燈號的控管；傳送給車輛駕駛人，可擬定最佳行車路線；又長期累積的交通資料，可提供道路維修與興建人員規劃設計之參考。此外，交通監控系統也可以用來偵測不尋常的交通事件，如碰撞、故障暫停、違規等。長期對重要交通樞紐的觀察，可瞭解其中的設施、駕駛人的行為、與意外發生之間的關係。諸如此類的應用，可謂不勝枚舉。
稍後將在文中描述本研究之交通監控系統的架構，並以一例子來說明整個架構的流程；其中較重要的部分再討論其細節作法。此外，整個系統發展過程中遭遇到的困難以及因應之道亦會在文中討論。

The importance of the transportation system to a country can be just like that of the blood circulation system to the human body. Many countries have spent a great amount of annual budget for maintaining, improving, and enhancing their transportation systems. It was common in the past that hardware resources (e.g., roads and vehicles) were introduced in order to meet the rapidly increasing requests on transportation. However, the former almost always hardly catches up with the latter. Moreover, introducing hardware equipments could be inadequate for the areas with limited sizes and crowded people. Therefore, under present conditions how to increase the efficacy, safety, and comfortability of transportation systems with the help of currently available high technologies, such as computers, networks, communication, controls, and advance managements may become more practicable.
Traffic monitoring systems collect such data as traffic flows, headways, road occupancies, average car speeds, driving directions and trajectories from main arteries and critical hinges. The collected data would be useful for traffic control centers to manage either manually or automatically traffic signals. Vehicle drivers will be able to plan in advance their routes before leaving for destinations based on the information delivered from traffic control centers. Long-term observed traffic data will provide valuable references for the personnels who maintain and construct roads as well as the researchers who would like to investigate the relationships among transportation equipments, driving behaviors, and accidents at important transit spots. Furthermore, some traffic surveillance systems can detect unusual events (e.g., collisions, breakdowns, and traffic law violations) and afterwards put on record the processes of the events. In the above, we only name a few applications regarding traffic monitoring systems. There are actually more to say with the systems.
In this project, a prototype of traffic surveillance system is proposed. The details of implementing the system are described in depth. We mention the difficulties probably encountered during the development of the system and provide possible solutions to the difficulties as well.

圖目錄iii
第一章 緒論1-1
　　1.1重要性1-1
　　1.2相關研究1-3
　　1.3論文的結構1-6
第二章 道路監控系統2-1
　　2.1前處理2-1
　　　　2.1.1背景影像的建立2-2
　　　　2.1.2路面圖的建立2-2
　　2.2前景物擷取2-3
　　2.3特徵萃取2-4
　　2.4車輛追蹤2-4
第三章 前處理3-1
　　3.1背景影像的建立3-2
　　　　3.1.1移除前景物體3-2
　　　　3.1.2計算背景值3-2
　　　　3.1.3填補空洞3-3
　　　　3.1.4彩色背景影像的建立3-4
　　3.2偵測路面區域3-4
　　　　3.2.1多層解析影像的形成3-5
　　　　3.2.2路面擷取3-6
　　　　3.2.3 m1m2m3彩色模式3-8
　　　　3.2.4解析度回復3-9
第四章 前景物擷取4-1
　　4.1取出前景物痕跡影像4-1
　　4.2局部可變二元化4-2
　　4.3取得完整區塊4-5
　　4.4背景影像更新4-5
第五章 特徵萃取5-1
　　5.1角偵測5-2
　　5.2 Top-view 轉換5-4
第六章 車輛追蹤6-1
　　6.1車輛追蹤程序6-1
　　6.2 Kalman Filters6-2
　　6.3鬆弛法(Relaxation)6-4
　　6.4向量的相似程度6-6
第七章 實驗7-1
第八章 結論8-1
附錄A 參考文獻A-1

[王01] 王俊明, 建構背景影像(尚未發表，預計發表於CVGIP2001), 2001.
[Bak92] K.D. Baker and G.D. Sullivan, Performance assessment of model-based tracking, Proceedings of IEEE Workshop on Applications of Computer Vision, pp.28-35, 1992.
[Bas97] E.K. Bas and J.D. Crisman, An Easy to Install Camera Calibration For Traffic Monitoring, IEEE Conference on Intelligent Transportation System, pp.362-366, 1997.
[Beu94] S. Beucher and M. Bilodeau, Road Segmentation and Obstacle Detection by a Fast Watershed Transformation, Proceedings of the Intelligent Vehicles ''94 Symposium, pp.296-301, 1994.
[Bey96] D. Beymer and J. Malik, Tracking Vehicles in Congested Traffic, Proceedings of the 1996 IEEE Intelligent Vehicles Symposium, pp.130-135, 1996
[Bey97] D. Beymer, P. McLauchlan, B. Coifman and J. Malik, A Real-time Computer Vision System For Measuring Traffic Parameters, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.495-501, 1997.
[Coi98] B. Coifman, D. Beymer, P. McLauchlan, J. Malik, A Real-time Computer Vision System for Vehicle Tracking and Traffic Surveillance, Transportation Research Part C: Emerging Technologies, Vol.6, Issue:4, pp.271-288, August 1998.
[Cri91] J.D. Crisman, C.E. Thorpe, UNSCARF-a color vision system for the detection of unstructured roads, Proceedings of the 1991 IEEE International Conference on Robotics and Automation, pp.2496-2501 vol.3, 1991.
[Cuc99] R. Cucchiara, M. Piccardi, P. Mello, Image Analysis and Rule-based Reasoning for a Traffic Monitoring System, Proceedings of the 1999 IEEE/IEEJ/JSAI International Conference on Intelligent Transportation Systems, pp.758-763, 1999.
[Dai99] D.J. Dailey, L. Li, An algorithm to estimate vehicle speed using uncalibrated cameras, Proceedings of IEEE/IEEJ/JSAI International Conference on Intelligent Transportation System, pp.441—446, 1999.
[Dub93] M.-P. Dubuisson, A.K. Jain, Object Contour Extraction Using Color And Motion, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.471-476, 1993.
[Dub95] M.-P. Dubuisson, S. Lakshmanan, A.K. Jain, Vehicle Segmentation and Classification Using Deformable Templates, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.18, Issue:3, pp.293—308, March 1996.
[Gia99] P. Giaccone, D. Amanatidis and G.A. Jones, Segmenting Image Sequences by Embedding Motion and Colour Cues Within a Contextural Relaxation Scheme, IEE Colloquium on Motion Analysis and Tracking (Ref. No. 1999/103), pp.18/1 -18/6, 1999
[Ger99] T. Gervers, A. W.M. Smeulders, Color-based Object Recognition, Pattern Recognition 32, pp.453-464, 1999.
[Gol99] J. Goldbeck, B. Huertgen, Lane detection and tracking by video sensors, Proceedings of 1999 IEEE/IEEJ/JSAI International Conference on Intelligent Transportation Systems, pp.74-79, 1999.
[Gui99] A. Guiducci, Parametric Model of the Perspective Projection of a Road with Applications to Lane Keeping and 3D Road Reconstruction, Computer Vision and Image Understanding Vol. 73, No. 3, March, pp.414-427, 1999.
[Ike96] T. Ikeda, S. Ohnaka, M. Mizoguchi, Traffic measurement with a roadside vision system-individual tracking of overlapped vehicles, Proceedings of the 13th International Conference on Pattern Recognition, Vol.3, pp.859-864, 1996.
[Iwa99] Y. Iwasaki, A measurement method of pedestrian traffic flows by use of image processing and its application to a pedestrian traffic signal control, Proceedings of IEEE/IEEJ/JSAI International Conference on Intelligent Transportation Systems, pp.310—313, 1999.
[Jai] Ramesh Jain, Rangachar Kasturi, Brian G. Schunck, Machine Vision, 1995
[Jun99] Y.-K. Jung, Y.-S. Ho, Traffic parameter extraction using video-based vehicle tracking, Proceedings of IEEE/IEEJ/JSAI International Conference on Intelligent Transportation Systems, pp.764 —769, 1999.
[Kil92] M. Kilger, A shadow handler in a video-based real-time traffic monitoring system, Proceedings of IEEE Workshop on Applications of Computer Vision, pp.11—18, 1992.
[Kim99] M. Kimachi, Y. Wu, and S. Ogata, A vehicle recognition method robust against vehicles'' overlapping based on stereo vision, Proceedings of 1999 IEEE/IEEJ/JSAI International Conference on Intelligent Transportation Systems, pp.865-869, 1999.
[Klu95] K. Kluge, S. Lakshmanan, A deformable-template approach to lane detection, Proceedings of the Intelligent Vehicles ''95 Symposium, pp.54-59, 1995.
[Kol93] D. Koller, J. Weber, and J. Malik, Robust Multiple Car Tracking with Occlusion Reasoning, Technical Report UCB/CSD-93-780, University of Calibornia at Berkely, October 1993.
[Kol94] D. Koller, J. Weber, and J. Malik, Towards Realtime Visual Based Tracking in Cluttered Traffic Scenes, Proceedings of the Intelligent Vehicles Symposium, Paris France, pp.201-206, Oct 24-26, 1994.
[Li99] C. Li, K. Ikeuchi, M. Sakauchi, Acquisition of traffic information using a video camera with 2D spatio-temporal image transformation technique, IEEE/IEEJ/JSAI International Conference on Intelligent Transportation Systems, pp.634 —638, 1999.
[Lia98] A.H.S. Lai, N.H.C. Yung, A Fast and Accurate Scoreboard Algorithm for Estimating Stationary Backgrounds in an image sequence, Proceedings of the 1998 IEEE International Symposium on Circuits and System, Vol. 4, pp241-244, 1998
[Mae96] Y. Mae, Y. Shirai, J. Miura, Y. Kuno, Object tracking in cluttered background based on optical flow and edges, Proceedings of the 13th International Conference on Pattern Recognition Volume: 1, pp.196—200, 1996.
[Mas99] O. Masoud, N.P. Papanikolopoulos and E. Kwon, Vision-Based Monitoring of Weaving Sections, IEEE/IEEJ/JSAI International Conference on Intelligent Transportation Systems, pp. 770-775, 1999.
[Mat00] Y. Matsushita, S. Kamijo, K. Ikeuchi and M. Sakauchi, Image Processing Based Incident Detection at Intersections, Proceedings of the Fourth Asian Conference on Computer Vision 2000, pp.520-527, 2000.
[Nak92] T. Nakanishi, K. Ishii, Automatic vehicle image extraction based on spatio-temporal image analysis, Proceedings of 11th IAPR International Conference on Pattern Recognition. Vol.1. Conference A: Computer Vision and Applications, pp.500-504, 1992.
[Nak79] Y. Nakcogawa and A. Rosenfeld, Some experiments of variable threshold, Pattern Recognition, Vol. 11, N. 3, pp.191-204, 1979.
[Sal00] L.Salgado, N.Garcia, Efficient Image Segmentation for Region-Based Motion Estimation and Compensation, IEEE Transactions on Circuits and Systems for Video Technology, VOL.10, No.7, pp.1029-1037, October 2000.
[Sch94] M. Schmid, An approach to model-based 3-D recognition of vehicles in real time by machine vision, Proceedings of the IEEE/RSJ/GI International Conference on Intelligent Robots and Systems ''94. ''Advanced Robotic Systems and the Real World'', IROS ''94. Vol. 3, pp.2064-2071, 1994.
[She93] A.J. Shelley, N.L. Seed, Approaches to static background identification and removal, IEE Colloquium on Image Processing for Transport Applications, pp.6/1-6/4, 1993.
[Smi96] C.E. Smith, C.A. Richards, S.A. Brandt, and N.P. Papanikolopoulos, Visual Tracking for Intelligent Vehicle-Highway Systems, IEEE Transaction on Vehicular Technology, Vol. 45, No. 4, pp.744-759, November 1996.
[Tak99] A. Takahashi, Y. Ninomiya, M. Ohta, K. Tange, A robust lane detection using real-time voting processor, Proceedings of 1999 IEEE/IEEJ/JSAI International Conference on Intelligent Transportation Systems, pp.577-580, 1999
[Wel95] G. Welch, B. Gary, An Introduction to the Kalman Filter, University of North Carolina at Chapel Hill, Department of Computer Science, Chapel Hill, NC, USA. TR95-041, 1995.
[Zhu96] Z. Zhu, B. Yang, G. Xu and D. Shi, A Real-Time Vision system for Automatic Traffic Monitoring Based on 2D Spatio-Temporal Image, Proceedings 3rd IEEE Workshop on Applications of Computer Vision, pp.162-167, 1996.