Responsive image
博碩士論文 etd-0910112-140351 詳細資訊
Title page for etd-0910112-140351
論文名稱
Title
基於單一影像之車輛偵測系統軟硬體設計
Software and Hardware Designs of a Vehicle Detection System Based on Single Camera Image Sequence
系所名稱
Department
畢業學年期
Year, semester
語文別
Language
學位類別
Degree
頁數
Number of pages
71
研究生
Author
指導教授
Advisor
召集委員
Convenor
口試委員
Advisory Committee
口試日期
Date of Exam
2012-07-24
繳交日期
Date of Submission
2012-09-10
關鍵字
Keywords
機器學習、行車輔助系統、圖形識別、影像處理、車輛偵測
Machine Learning, Vehicle Detection, Pattern Recognition, Image Processing, Driving Assistance System, Support Vector Machine (SVM)
統計
Statistics
本論文已被瀏覽 5670 次,被下載 4168
The thesis/dissertation has been browsed 5670 times, has been downloaded 4168 times.
中文摘要
本論文提出一基於單一影像序列影像處理之車輛偵測追蹤系統,並以軟硬體設計及實作。在假定區域產生與驗證階段,本論文基於陰影偵測法﹐結合車寬與距離比例之驗證,可成功濾除不合理之假定區域;另外亦採用機器學習方式以Support Vector Machine (SVM)分類器驗證假定區域﹐提升車輛辨識之精確度。在車輛追蹤階段,我們設計了車輛區域記錄過期以及偵測週期機制以避免追蹤錯誤的影響,使得系統不需在每個畫格進行較為複雜之車輛偵測程序﹐以提升處理效率。本論文根據軟體執行結果﹐找出費時最久的部份﹐針對影像前處理之亮度轉換與邊緣偵測運算﹐提出硬體架構,並以FPGA實作嵌入式軟硬體協同運算系統,使得系統效率在不增加太多硬體成本前提下﹐能進一步提升﹐達到即時處理的速度要求。
Abstract
In this thesis, we present a vehicle detection and tracking system based on image processing and pattern recognition of single camera image sequences. Both software design and hardware implementation are considered. In the hypothesis generation (HG) step and the hypothesis verification (HV) step, we use the shadow detection technique combined with the proposed constrained vehicle width/distance ratio to eliminate unreasonable hypotheses. Furthermore, we use SVM classifier, a popular machine learning technique, to verify the generated hypothesis more precisely. In the vehicle tracking step, we limit vehicle tracking duration and periodic vehicle detection mechanisms. These tracking methods alleviate our driver-assistant system from executing complex operations of vehicle detection repeatedly and thus increase system performance without sacrificing too much in case of tracking wrong objects. Based on the the profiling of the software execution time, we implement by hardware the most critical part, the preprocessing of intensity conversion and edge detection. The complete software/hardware embedded system is realized in a FPGA prototype board, so that performance of whole system could achieve real-time processing without too much hardware cost.
目次 Table of Contents
中文論文審定書 I
英文論文審定書 II
中文摘要 V
Abstract VII
第1章 概論 1
1.1 本文大綱 1
1.2 研究動機 1
第2章 研究背景與相關研究 3
2.1 車輛偵測系統概述 3
2.2 基於影像處理之車輛偵測系統組成 3
2.3 假定區域產生方法相關研究 5
2.3.1 陰影偵測法 5
2.3.2 對稱偵測法 6
2.3.3 水平/垂直邊緣偵測法 7
2.3.4 其他偵測法 8
2.4 假定區域驗證方法相關研究 9
2.4.1 模板驗證法 9
2.4.2 外觀預測驗證法 9
2.5 其他相關研究 12
2.5.1 影像平滑化前處理 12
2.5.2 消失點偵測 12
2.6 相關研究總結 13
第3章 演算法流程及架構 15
3.1 名詞與參數定義 15
3.1.1 偵測區域定義 15
3.1.2 偵測參數定義 16
3.2 系統流程 18
3.3 前置影像處理 19
3.3.1 亮度圖轉換 19
3.3.2 邊緣偵測 20
3.3.3 陰影門檻計算 21
3.4 車輛區域偵測(HG) 23
3.5 車輛區域驗證(HV) 25
3.5.1 比例驗證 25
3.5.2 支援向量機驗證 26
3.6 車輛追蹤 31
第4章 硬體架構設計 33
4.1 硬體架構設計 33
4.2 軟硬體協同運算架構 35
第5章 實驗結果及分析 37
5.1車輛偵測效果與分析 37
5.1.1 車輛偵測效果 37
5.1.2 車輛偵測結果分析 39
5.2軟體執行耗時分析 43
5.3軟硬體協同運算執行耗時分析 46
第6章 結論 49
參考文獻 50
參考文獻 References
[1]. Daniel Scharstein, “ View Synthesis Using Stereo Vision”, Dissertation of Cornell University PHD, 1997.
[2]. Daniel Scharstein and Richard Szeliski ,”A Taxonomy and Evaluation of Dense Two-Frame. Stereo Correspondence Algorithms”, International Journal of Computer Vision 47(1/2/3), pp. 7–42, 2002.
[3]. Chul-Hwan Kim*, Ho-Keun Lee, and Yeong-Ho Ha ,”Disparity space image based stereo matching using optimal path searching”, Image and Video Communications and Processing, pp. 752-760, 2003.
[4]. Chun-Jen Tsai and Aggelos K. Katsaggelos ,“Dense Disparity Estimation with a Divide-and-Conquer Disparity Space Image Technique”, IEEE TRANSACTIONS ON MULTIMEDIA, VOL. 1, NO. 1, MARCH, pp. 18-29, 1999.
[5]. Pei-Yung Hsiao, Chia- Chen Hsu, Chia- Haw Hsu, Shih-Shinh Huang, and Wei-Yuan Wang,“A Visual Based Lane Departure Warning System with Vertical Shifting Accelerators”, VLSI Design / CAD Symposium 2011.
[6]. S. Jin, J. Cho, X. D. Pham, K. M. Lee, S.-K. Park, M. Kim, and J. W. Jeon. “FPGA Design and Implementation of a Real-Time Stereo Vision System”, IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, VOL. 20, NO. 1, pp. 15-26, 2010.
[7]. Khurram Jawed, John Morri Tariq Khan and Georgy Gimel’farb, “High Resolution Real-time Stereophotogrammetry”, Lecture Slide of The University of Auckland New Zealand.
[8]. Stephen S. Intille and Aaron F. Bobick , “Disparity-space images and large occlusion stereo”, Computer Vision - ECCV 94, pp. 179-186.
[9]. Ruigang Yang and Marc Pollefeys, “Multi-Resolution Real-Time Stereo on Commodity Graphics Hardware”, CVPR, pp. 211, 2003.
[10]. 張 懿,“即時路面標線、車輛偵測與距離估計”,淡江大學-碩士論文 ,2002.
[11]. Zehang Sun, George Bebis and Ronald Miller ,“On-Road Vehicle Detection: A Review”, IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, pp. 694-711, 2006.
[12]. A. Bensrhair, M. Bertozzi, A. Broggi, P. Mich’ e, S. Mousset, and G. Toulminet ,“A Cooperative Approach to Vision-based Vehicle Detection“, IEEE Intelligent Transportation Systems Conference Proceedings, pp. 209-214, 2001.
[13]. Istitutodi Elettronicae Telecomunicazioni ,”Visual Perception of Obstacles and Vehicles for Platooning”, IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, pp. 164-176, 2000.
[14]. Zehang Sun, George Bebis and Ronald Miller, ”Using Symmetry for Detecting and Locating Objects in a Picture“, COMPUTER VISION, GRAPHICS, AND IMAGE PROCESSING, pp.179-195 , 1989.
[15]. Thomas Zielke Michael Brauckmann Werner von Seelen, ”CARTRACK: Computer Vision-Based Car-Following”, IEEE Applications of Computer Vision, Proceedings, pp.156-163, 1992.
[16]. Christian Hoffmann, Thao Dang, Christoph Stiller , “Vehicle detection fusing 2D visual features”, IEEE Intelligent Vehicles Symposium, pp280-285, 2004.
[17]. Zehang Sun, George Bebis, and Ronald Miller,“Monocular Precrash Vehicle Detection: Features and Classifiers”, IEEE TRANSACTIONS ON IMAGE PROCESSING, pp. 2019-2034 , 2006.
[18]. Xi Yong, Liwei Zhang ,“Real-time Vehicle Detection Based on Haar Features and Pairwise Geometrical Histograms”, Proceeding of the IEEE International Conference on Information and Automation, pp. 390-395, 2011.
[19]. Massimo Bertozzi, Alberto Broggi, Stefano Castelluccio ,“A real-time oriented system for vehicle detection”, Journal of Systems Architecture, pp.318-325, 1997.
[20]. Zehang Sun, Ronald Miller, George Bebis, and David DiMeo,“A Real-time Precrash Vehicle Detection System”, IEEE Workshop of CV, pp.171-176, 2002.
[21]. Gwenaelle Toulminet, Massimo Bertozzi, Stephane Mousset, Abdelaziz Bensrhair, and Alberto Broggi , “Vehicle Detection by Means of Stereo Vision-Based Obstacles Features Extraction and Monocular Pattern Analysis”, IEEE TRANSACTIONS ON IMAGE PROCESSING, pp.2364-2375, 2006.
[22]. Marola, “Using Symmetry for Detecting and Locating Objects in a Picture,” Computer Vision, Graphics, and Image Processing, vol. 46, pp. 179-195, 1989
[23]. A. Kuehnle, “Symmetry-Based Recognition for Vehicle”, RePattern Recognition Letters, vol. 12, pp. 249-258, 1991.
[24]. T. Zielke,M. Brauckmann, andW. von Seelen, “Intensity and Edge-Based Symmetry Detection with an Application to Car-Following”, CVGIP: Image Understanding, vol. 58, pp. 177-190, 1993.
[25]. Quoc-Bao Truong1 and Byung-Ryong Lee, “New Lane Detection Algorithm for Autonomous Vehicles Using Computer Vision”, International Conference on Control, Automation and Systems, pp1208-1213, 2008.
[26]. Zehang Sun, “On-road vehicle detection using evolutionary Gabor filter optimization”, IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, VOL. 6, NO. 2, pp126-137, 2005.
[27]. Khronos OpenCL Working Group , “ OpenCL 1.2 Specification ” , November 15, 2011
[28]. N. Matthews, P. An, D. “ Charnley, and C. Harris, “Vehicle Detection and Recognition in Greyscale Imagery,” Control Eng. Practice, vol. 4, pp. 473-479, 1996.
[29]. T. Kalinke, C. Tzomakas, and W. von Seelen, “A Texture-Based Object Detection and an Adaptive Model-Based Classification,” Proc. IEEE Int’l Conf. Intelligent Vehicles, pp. 143-148, 1998
[30]. J. Crisman and C. Thorpe, “Color Vision for Road Following,” Proc. SPIE Conf. Mobile Robots, pp. 246-249, 1988.
[31]. R. Cucchiara and M. Piccardi, “Vehicle Detection under Day and Night Illumination,” Proc. Int’l ICSC Symp. Intelligent Industrial Automation, 1999.
[32]. Stephen P. Tseng and Derek Fong, “A DSP Based Real-Time Front Car Detection Driving Assistant System”, SICE Annual Conference, pp.2419-2423, 2010.
[33]. Houjie Xu and Huifang Li, “Study on a Robust Approach of Lane departure Warning Algrithm”, International Conference on Signal Processing Systems, pp.201-204, 2010.
[34]. Jianzhu Cui,Fuqiang Liu,Zhipeng and Li,Zhen Jia, “Vehicle Localisation Using a Single Camera”, IEEE Intelligent Vehicles Symposium, pp.871-876, 2010.
[35]. Ying-Che Kuo and Hsuan-Wen Chen, “Vision-based Vehicle Detection in the Nighttime”, International Symposium on Computer, Communication, Control and Automation, pp.361-364, 2010.
[36]. Chi-Feng Wu, Cheng-Jian Lin, and Chi-Yung Lee, “Applying a Functional Neurofuzzy Network to Real-Time Lane Detection and Front-Vehicle Distance Measurement”, IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART C: APPLICATIONS AND REVIEWS, IEEE Transactions on Systems, Man, and Cybernetics, pp.1-13, 2011.
[37]. Pei-Hsuan Yuan, Kuo-Feng Yang, and Wen-Hsiang Tsai, “Real-Time Security Monitoring Around a Video Surveillance Vehicle With a Pair of Two-Camera Omni-Imaging Devices”, IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 60, NO. 8, pp.3603-3614, 2011.
[38]. H. Mallot, H. Bulthoff, J. Little, and S. Bohrer, “Inverse Perspective Mapping Simplifies Optical Flow Computation and Obstacle Detection,” Biological Cybernetics, vol. 64, no. 3, pp. 177-185, 1991.
[39]. Aditya Kanitkar, Brijendra Bharti and Umesh N. Hivarkar, “Vision based Preceding Vehicle Detection Using Self Shadows and Structural Edge Features”, ICIIP, 2011.
[40]. P. Parodi and G. Piccioli, “A Feature-Based Recognition Scheme for Traffic Scenes,” Proc. IEEE Intelligent Vehicles Symp., pp. 229-234, 1995.
[41]. U. Handmann, T. Kalinke, C. Tzomakas, M. Werner, and W.Seelen, “An Image Processing System for Driver Assistance,” Image and Vision Computing, vol. 18, no. 5, 2000.
[42]. N. Cristianini and J. Shawe-Taylor, “An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods.” Cambridge, U.K.: Cambridge Univ. Press, 2000.
[43]. C. Cortes and V. N. Vapnik, “Support Vector Networks,” Mach. Learn.,vol. 20, no. 3, pp. 273–297, Sep. 1995.
[44]. Chih-Chung Chang and Chih-Jen Lin, "LIBSVM -- A Library for Support Vector Machines," http://www.csie.ntu.edu.tw/~cjlin/libsvm/
[45]. Bureu Aytekin, Erdin9 Altug, "Increasing Driving Safety with a Multiple Vehicle Detection and Tracking System using Ongoing Vehicle Shadow Information", IEEE Systems Man and Cybernetics (SMC), pp3650 - 3656 ,2010.
[46]. Thorsten Suttorp, Thomas B‥ucher, “Robust Vanishing Point Estimation for Driver Assistance”, Proc. of the IEEE Intelligent Transportation Systems Conference ,2006.
[47]. G.McLean, D.Kotturi, “Vanishing point detection by line clustering,” IEEE Transactions on Pattern Analysis and Machine Intelligence,vol. 17, no. 11, pp. 1090–1095, 1995.
電子全文 Fulltext
本電子全文僅授權使用者為學術研究之目的,進行個人非營利性質之檢索、閱讀、列印。請遵守中華民國著作權法之相關規定,切勿任意重製、散佈、改作、轉貼、播送,以免觸法。
論文使用權限 Thesis access permission:自定論文開放時間 user define
開放時間 Available:
校內 Campus: 已公開 available
校外 Off-campus: 已公開 available


紙本論文 Printed copies
紙本論文的公開資訊在102學年度以後相對較為完整。如果需要查詢101學年度以前的紙本論文公開資訊,請聯繫圖資處紙本論文服務櫃台。如有不便之處敬請見諒。
開放時間 available 已公開 available

QR Code