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研究生:吳耀然
研究生(外文):Yao-Jan Wu
論文名稱:電腦視覺為基礎之交通辨識技術應用於車載駕駛輔助與交通監控研究
論文名稱(外文):Computer Vision-Based Traffic Identification Technologies for On-board Driving Assistance and Traffic Monitoring
指導教授:張堂賢張堂賢引用關係
指導教授(外文):Tang-Hsien Chang
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:土木工程學研究所
學門:工程學門
學類:土木工程學類
論文種類:學術論文
論文出版年:2004
畢業學年度:92
語文別:英文
論文頁數:164
中文關鍵詞:交通監控系統電腦視覺行車駕駛輔助系統車輛偵測車道線偵測
外文關鍵詞:Traffic Monitoring SystemComputer VisionDriver Assistance SystemLane Markings DetectionVehicle Detection
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本研究之目的係提出一個電腦視覺為基礎之公路安全系統架構。此架構主要包含三個部分:電腦視覺為基礎之行車駕駛輔助系統 (Computer Vision-based Driver Assistance System, CVDAS)、電腦視覺為基礎之交通監控系統 (Computer Vision-based Traffic Monitoring System, CVTMS)以及用路人。上述二系統將利用電腦視覺及影像處理技術達到資訊取得之目的,並驗證其效能及可行性。
本研究所發展之行車安全輔助系統主要利用架設於車上之CCD (Charge Couple Device, 光電耦合元件) 攝影機取得行車前方影像,再運用電腦視覺及影像處理技術,於一般公路系統中識別行車環境。系統運作主要包含兩個步驟,依序為車道線偵測 (lane detection) 以及前方多車偵測 (multiple vehicle detection)。首先,透過車道線偵測可取得車輛與車道線之相對關係及構建空間資訊,並有效利用 濾波器推估行車軌跡。之後,利用已取得之道路空間資訊,進而有效偵測前方行駛車輛,並取得其相對位置資訊。本研究結果顯示,車道線偵測可有效取得行車軌跡線,且車輛偵測的平均成必v可達97%以上。
本研究所發展之交通監控系統,乃利用架設於路側之CCD攝影機擷取車流影像並進行影像處理,其主要包含五步驟: (1) 前處理,(2)前景取出,(3) 陰影消除,(4)車輛追蹤,(5)交通參數之取得。於前處理中,採用自動或手動車道線偵測弁遄A對攝影機進行自動校正並構建空間資訊。系統運作時,在可視範圍內之移動車輛會被視為前景而取出,並同時進行陰影去除。於車輛追蹤時,本系統採用 濾波器加強車輛追蹤之強健性。最後,車流基本參數即可取得。根據實驗結果顯示,車輛追蹤及偵測區域車流量之偵測成必v高於96%。本研究結果證實,CCD攝影機搭配影像處理技術可以成巨�得車流參數以及多車行車軌跡。
This study presents a conceptual architecture of computer vision based highway safety architecture that consists of three parts: Computer Vision-based Driver Assistance System (CVDAS), Computer Vision-based Traffic Monitoring System (CVTMS) and road users. In this study, CVTMS and CVDAS are developed and validated, respectively.
The CVDAS developed in this study is mainly to identify the driving environment for autonomous highway vehicles by employing image processing and computer vision techniques. The proposed approach is composed of two consecutive computational steps. The first step is the lane markings detection, used to identify the location of the equipped vehicle and road geometry. The driving trajectory of the equipped vehicle is estimated by a filter. The second step is the multiple vehicle detection that can provide relative position and speed between the equipped vehicle and its preceding vehicle. The experimental results revealed that the success rate of vehicle detection is higher than 97%.
The CVTMS developed in this study is mainly composed of five stages: (1) pre-processing, (2) foreground segmentation, (3) shadow removal, (4) tracking and (5) traffic parameters extraction. The pre-processing is developed to obtain the information of road geometry and calibrate the camera. After the preprocessing is done, the foreground segmentation and shadow removal continue to segment the moving vehicles from the input images. To make the system more robust, a filter is used in the multi-vehicle tracking. Subsequently, the traffic parameters are extracted at the end of each tracking. According to the results, the average success rate of vehicle counting is higher than 96 %. Moreover, it shows that this system is capable of successfully extracting the traffic parameters, including trajectory of the moving vehicle based on the image sequences captured by a CCD (Charge Couple Device) camera.
誌謝 I
摘要 III
ABSTRACT V
TABLE OF CONTENTS VII
LIST OF FIGURE XI
LIST OF TABLES XVII
Chapter One Introduction 1
1.1 Background and Motivation 1
1.2 Objective 3
1.3 Literature Review 5
1.3.1 Computer Vision-based Driver Assistance System 6
1.3.2 Computer Vision-based Traffic Monitoring System 8
1.3.3 Summary 11
1.4 Thesis Organization 12
Chapter Two Foundamentals of Image Processing 13
2.1 Introduction 13
2.2 Image Acquisition Devices 13
2.3 Fundamental Image Processing Methods 14
2.3.1 Denoise 15
2.3.2 Edge Detection 16
2.3.3 Morphological operation 18
2.3.4 Connected Component Labeling 19
Chapter Three Computer Vision-based Driver Assistance System 20
3.1 Introduction 20
3.2 System Configuration 22
3.3 Lane Markings Detection 23
3.3.1 Introduction 23
3.3.2 Define the Searching Region 26
3.3.3 Connect the Dashed Lane Markings 26
3.3.4 Segmentation 28
3.3.5 Line Fitting 29
3.3.6 Driving Environment Building 31
3.3.7 Measurement of Lateral Displacement and Lateral Speed 32
3.4 Multiple Vehicle Detection 34
3.4.1 Introduction 34
3.4.2 Pre-processing Step 35
3.4.3 Primary Search Step 37
3.4.4 Refined Search Step 39
3.4.5 Verification and Enhancement Step 42
3.4.6 Identification of Driving Environment 42
3.5 Experiments 46
3.5.1 Description 46
3.5.2 Lane Detection 47
3.5.3 Vehicle Detection 54
3.5.4 Discussion 56
3.5.5 Summary 59
Chapter Four Computer Vision-based Traffic monitoring System 62
4.1 Introduction 62
4.2 System Overview 66
4.2.1 System Configuration 66
4.2.2 System Architecture 68
4.3 Pre-processing 72
4.3.1 Background Image Construction 72
4.3.2 Preliminary of Lane Detection 76
4.3.3 Lane Detection 79
4.3.4 Camera Calibration 93
4.3.5 Discussion 97
4.4 Vehicle Detection 98
4.4.1 Introduction 98
4.4.2 Foreground Segmentation 100
4.4.3 Shadow Removal 105
4.4.4 Discussion and Evaluation 115
4.5 Vehicle Tracking 116
4.5.1 Introduction 116
4.5.2 Region Splitting 119
4.5.3 Region Grouping 120
4.5.4 Tracking 125
4.5.5 Traffic Parameters Extraction 130
4.6 Experiments 133
4.6.1 Description 133
4.6.2 Experimental Results 134
4.6.3 Discussion 141
Chapter Five Conclusions and Future works 145
5.1 Conclusions 145
5.1.1 Conclusion of CVDAS 145
5.1.2 Conclusion of CVTMS 146
5.2 Future Works 148
5.2.1 Future work for CVDAS 148
5.2.2 Future work for CVTMS 149
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