傅立成臺灣大學：資訊工程學研究所詹益銘Chan, Yi-MingYi-MingChan2007-11-262018-07-052007-11-262018-07-052005http://ntur.lib.ntu.edu.tw//handle/246246/53642在台灣每年有將近兩千五百人因道路交通事故而死亡，其中肇事時間發生在晚上的比例為53%。分析其肇事原因，最主要的原因為「不適當的駕駛行為」，疲勞駕車或是手持行動電話等行為皆屬於此範疇之內。有鑒於此，本論文係發展一套以電腦視覺技術為基礎之駕駛輔助系統，系統藉由適應於各種光源下之車道偵測與汽車辨識來避免這兩種主要危害，以確保駕駛員行車的安全。 本論文的目標在於應用電腦視覺的技術來偵測車道線與前方車輛。在車道線偵測方面，利用了三個車道線的特徵 高亮度、細長度與連續性，開發了山峰偵測演算法(Hill Detection Algorithm)來找出畫面中符合車道線特徵之山峰。結合高斯濾波器、山峰偵測與線段的結合，得以完成車道線的偵測。在汽車偵測部份，我們使用粒子濾波器(Particle Filtering)結合四條線索，分別名為垂直邊、車尾燈、車底陰影與水平對稱性。另外，本論文也提出透過消失點偵測達成自動校正相機外部參數之俯仰角(Tilt)與搖動角(Yaw)之方法。同時，也可以由車道線端點偵測，找到依法規製作固定長度之車道線，藉此推算相機高度。 本論文提出之系統於車道偵測率可達97%，證明了本系統的可靠性。此外，由於計算複雜度低，每秒可以處理12到20張畫面滿足即時系統的需求，也提高了本系統的實用性。In Taiwan, more than 2,500 people die in the fatal traffic accidents per year, of which 53% traffic accidents happen in the nighttime. Besides, the major cause of traffic accidents is “Improper Driving” due to driver’s inattention or fatigue. For this reason, we develop a vision based driver assistance system which has capabilities of lane departure prevention and collision avoidance under various lighting condition. To detect the lane boundaries and vehicles by applying computer vision techniques are the objectives of this paper. In lane detection, three procedures including Gaussian filter, Peak-Finding Algorithm, and Line-Segment Grouping, based on three properties, brightness, slenderness, and continuity, are used to detect lane markings. In vehicle detection, we apply particle filtering with four cues, namely, Vertical Edge Cue, Taillight Cue, Underneath Cue and Symmetry Cue. Besides, in this paper, we also provide an automatic method to compute the pitch and the yaw angle of the camera according to the coordinate of vanishing point in the image. At the same time, we can compute the camera height by detecting the lane markings end points with fixed distance. The proposed system is shown to work well on highway. The detection rate in lane detection is nearly 97%. Besides, the computation cost of our approach is low and our system can process the image in almost real time.Chapter 1 Introduction 1 1.1 MOTIVATIONS 1 1.2 RELATED WORKS 4 1.3 OBJECTIVES 5 1.4 SYSTEM OVERVIEW 6 1.5 THESIS ORGANIZATION 8 Chapter 2 Camera Calibration 9 2.1 OVERVIEW 9 2.1.1 Motivation 9 2.1.2 Related Works 10 2.1.3 Overview of Camera Calibration 12 2.2 CAMERA MODEL 13 2.3 ON-LINE CAMERA HEIGHT ESTIMATION 16 2.4 CAMERA CALIBRATION PROCEDURE 18 2.4.1 Vanishing Point Detection 18 2.4.2 Lane Marking Extraction 19 2.5 SUMMARY 19 Chapter 3 Lane Detection and Tracking 20 3.1 OVERVIEW 20 3.1.1 Motivation 20 3.1.2 Related Works 20 3.1.3 Overview of Lane Detection and Tracking 22 3.2 LANE BOUNDARY CONSTRUCTION 23 3.3 LANE DETECTION AND TRACKING 27 3.4 SUMMARY 28 Chapter 4 Vehicle Detection and Tracking Using Particle Filters 30 4.1 OVERVIEW 30 4.1.1 Motivation 30 4.1.2 Related Works 30 4.1.3 Overview of Vehicle Detection and Tracking 31 4.2 PARTICLE FILTERS FOR VEHICLE TRACKING 33 4.2.1 Why Use Particle Filters? 33 4.2.2 Particle Filtering Initial Sampling and Propagation 34 4.3 CUE FUSION FOR PARTICLE FILTERS 37 4.4 VEHICLE DETECTION AND TRACKING PROCEDURE 42 4.5 HAZARD IDENTIFICATION 44 4.5.1 Lane Departure Prevention Mechanism 44 4.5.2 Collision Avoidance Mechanism 46 4.6 SUMMARY 47 Chapter 5 Experiment 49 5.1 ENVIRONMENT DESCRIPTION 49 5.2 CAMERA CALIBRATION 50 5.3 LANE DETECTION 52 5.4 VEHICLE RECOGNITION 54 5.5 PERFORMANCE 56 5.5.1 Detection Rate 56 5.5.2 Processing Time 56 Chapter 6 Conclusion 582409537 bytesapplication/pdfen-US智慧型運輸系統駕駛輔助系統電腦視覺粒子濾波器夜間車道偵測汽車辨識消失點相機校正高斯濾波器車尾燈碰撞時間ITSDriver Assitance SystemParticle FilteringParticle FiltersNight VisionLane DetectionVehicle RecognitionVanishing PointCamera CalibrationGaussian FilterTaillightTTCTLC[SDGs]SDG3thesishttp://ntur.lib.ntu.edu.tw/bitstream/246246/53642/1/ntu-94-R92922052-1.pdf