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研究生:李濠欣
研究生(外文):Hao-Hsin Li
論文名稱:即時相機校正之前車偵測
論文名稱(外文):Front Car Detection with Real-Time Camera Calibration
指導教授:丁肇隆丁肇隆引用關係
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:工程科學及海洋工程學研究所
學門:工程學門
學類:綜合工程學類
論文種類:學術論文
論文出版年:2009
畢業學年度:97
語文別:英文
論文頁數:62
中文關鍵詞:行車輔助系統車道偵測車輛偵測相機校正
外文關鍵詞:DASLane DetectionCar DetectionCamera Calibration
相關次數:
  • 被引用被引用:2
  • 點閱點閱:273
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:2
近年來世界各國漸漸的將智慧型運輸系統(Intelligent Transport System, ITS)視為重點發展項目。智慧型運輸系統的主要目的就是增進安全並且減少車輛、運輸時間以及燃料上的耗損。而車輛主動式安全系統 (Vehicle Active Safety System, VASS)是屬於智慧型運輸系統其中一環,利用架設在車輛上的感測器去收集週遭訊息並提醒駕駛人潛在的危險以減少意外事故之發生。本系統的目標是發展一套智慧型的行車輔助系統(Driver Assistance System, DAS)。藉由裝設在實驗車輛上的攝影機,配合電腦視覺及影像處理技術達成辨識車道線以及前方車輛的目的。在車道線偵測方面,我們採用了三個特徵,分別是高亮度、細長性及連續性特徵來設計演算法。而在前車偵測方面,由於白天及夜間的特性不同,我們分別使用車底陰影、車輛邊緣及車尾燈做為我們辨識的依據。除此之外,我們還利用消失點在影像上的特性,開發一套自動相機校正機制,進而提高距離估算的準確性。實驗結果顯示,車道偵測率接近99%,而車輛辨識率也可高達96%。這結果也顯示出我們所提出的演算法是可以有效地滿足實際上的需求。
In recent years, Intelligent Transportation System (ITS) has drawn more and more attention in the world. The goal of ITS is to improve traffic safety and to reduce transportation times and fuel consumption of vehicles. Vehicle Active Safety System (VASS) is one subject of ITS. By different kinds of sensors mounted on vehicles, it can collect surrounding messages and notice drivers to avoid potential hazards. Our objective is to research and develop an intelligent Driver Assistance System (DAS), which is one kind of VASS. This system utilizes a monocular camera mounted on the experimental car, and applies computer vision and image processing techniques to detect lane markings and front vehicles. In the lane detection section, we combine three lane markings features: brightness, slenderness and continuity to design our algorithm. In the front car detection section, shadow beneath a car, vertical edges of car sides and taillight positions are used to recognize front car positions in daytime and nighttime respectively. In order to evaluate the relative distance of objects more exactly, we address an automatic camera calibration method based on the vanishing point location. The experimental results show that the recognition rate of lane detection approximate 99% and the recognition rate of front car detection is about 96%. It is concluded that the proposed recognition algorithm works effectively and very well.
口試委員會審定書 I
致謝 II
ABSTRACT III
中文摘要 IV
TABLE OF CONTENTS III
LIST OF FIGURES V
LIST OF TABLES VII
CHAPTER 1 INTRODUCTION 1
1.1 MOTIVATION AND OBJECTIVE 1
1.2 LITERATURE REVIEW 3
1.3 SYSTEM OVERVIEW 5
1.4 THESIS ORGANIZATION 7
CHAPTER 2 CAMERA CALIBRATION 8
2.1 OVERVIEW 8
2.2 CAMERA CONFIGURATION 8
2.3 APERTURE ESTIMATION 14
2.4 DYNAMIC CAMERA CALIBRATION TECHNIQUE 17
2.4.1 Vanishing Point Estimation 17
2.4.2 Camera Parameters Estimation 18
CHAPTER 3 LANE DETECTION AND TRACKING 21
3.1 OVERVIEW 21
3.2 LANE MARKINGS EXTRACTION 22
3.2.1 ROI Creation 22
3.2.2 Brightness Filter 23
3.2.3 Edge Extraction 25
3.3 LANE BOUNDARY RECONSTRUCTION 25
3.3.1 Hough Transform 26
3.3.2 Driving Lane Construction 28
3.4 DRIVING LANE TRACKING 30
3.4.1 Direction Seeking Algorithm 30
3.4.2 Lane Change Handler 32
CHAPTER 4 FRONT CAR DETECTION 36
4.1 OVERVIEW 36
4.2 DAYTIME CASE 38
4.2.1 Lane Filter 38
4.2.2 Hypothesis Generation 39
4.2.3 Hypothesis Verification 40
4.2.4 Cut-in Situation 42
4.2 NIGHTTIME CASE 44
4.3.1 Scene Analysis 44
4.3.2 Hypothesis Generation in Nighttime 45
4.3.3 Hypothesis Verification in Nighttime 46
CHAPTER 5 RESULT AND DISCUSSION 48
5.1 FACILITY AND SETUP 48
5.2 CAMERA CALIBRATION RESULT 49
5.3 LANE DETECTION RESULT 51
5.4 FRONT CAR DETECTION RESULT 54
5.5 COMPUTATION COST 57
CHAPTER 6 CONCLUSION AND SUGGESTION 59
REFERENCE 61
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