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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/41403完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 丁肇隆 | |
| dc.contributor.author | Hao-Hsin Li | en |
| dc.contributor.author | 李濠欣 | zh_TW |
| dc.date.accessioned | 2021-06-15T00:18:18Z | - |
| dc.date.available | 2012-05-12 | |
| dc.date.copyright | 2009-05-12 | |
| dc.date.issued | 2009 | |
| dc.date.submitted | 2009-04-03 | |
| dc.identifier.citation | [1] National Police Agency, ROC, http://www.npa.gov.tw
[2] M. Bertozzi, and A. Broggi, “GOLD: a parallel real-time stereo vision system for generic obstacle and lane detection,” IEEE Trans. on Image Processing, Vol.7, No.1, pp.62-81, Jan. 1998. [3] T. Bücher, “Measurement of distance and height in images based on easy attainable calibration parameters,” in Proc. IEEE Intelligent Vehicles Symp., pp.314-319, Oct. 2000. [4] S. Zehang, R. Miller, G. Bebis, and D. DiMeo, “A real-time precrash vehicle detection system,” in Proc. IEEE Conf. on Applications of Computer Vision, Orlando, FL, pp.171-176, Dec.3-4, 2002. [5] Y. Wang, E. K. Teoh, and D. Shen, “Lane detection and tracking using B-Snake,” Image and Vision Computing, vol. 22, pp. 269-280, 2004. [6] W. Liu, X. Z. Wen, B. Duan, H. Yuan, and N. Wang, “Rear Vehicle Detection and Tracking for Lane Change Assist,” Intelligent Vehicles Symposium, 2007 IEEE , pp.252-257, June, 2007. [7] H. Lin, S. Ko, S. Wang, Y. Kim, and H. Kim, “Lane departure identification on Highway with searching the region of interest on Hough space,” International Conference on Control, Automation and Systems, COEX, Seoul, Korea, Oct. 17-20, 2007. [8] S. Zehang, G. Bebis, and R. Miller, “On-Road Vehicle Detection: A Review,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 5, pp. 694-711, May, 2006. [9] M. Bertozzi, A. Broggi, G. Conte, and A. Fascioli, “The experience of the ARGO autonomous vehicle,” in Proc. SPIE’98-Enhanced and Synthetic Vision, Orlando, FL, Apr.13-17, 1998. [10] M. Bertozzi, A. Broggi, G. Conte, and A. Fascioli, “Obstacle and lane detection on ARGO,” in Proc. IEEE Intelligent Transportation Systems Conference'97, Boston, pp.1010-1015, Nov.10-13, 1997. [11] Betke, M., E. Haritaoglu, and L. S. Davis, “Real-time multiple vehicle detection and tracking from a moving vehicle,” Machine Vision and Applications, vol.12, no.2, pp.69-83, Sep. 2000. [12] C. C. Wang, S. S. Huang, and L. C. Fu, “Driver assistance system for lane detection and vehicle recognition with night vision,” in IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3530-3535, 2005. [13] S. S. Huang, C. J. Chen, P. Y. Hsiao, and L. C. Fu, “On-Board Vision System for Lane Recognition and Front-Vehicle Detection to Enhance Driver’s Awareness,” IEEE Int. Conf. on Robotics and Automation, pp. 2456-2461, New Orleans, Los Angeles, USA, 2004. [14] H. Y. Cheng, B. S. Jeng, P. T. Tseng, and K. C. Fan, “Lane Detection With Moving Vehicles in the Traffic Scenes,” IEEE Transactions on Intelligent Transportation Systems, vol. 7, pp. 571-582, 2006. [15] Y. L. Chen, Y. H. Chen, C. J. Chen, and B. F. Wu, “Nighttime vehicle detection for driver assistance and autonomous vehicles,” in Proc. IEEE 18th Int’l Conf. on Pattern Recognition, Hong Kong, China, pp.687-690, Aug.20-24, 2006. [16] J. C. McCall, and M. M. Trivedi, “Video Based Lane Estimation and Tracking for Driver Assistance: Survey, System, and Evaluation,” IEEE Transactions on Intelligent Transportation Systems,, Vol. 7, No. 1, Mar. 2006. [17] X. Ma, and I. Andrasson, “Behavior measurement, analysis and regime classification in car-following,” IEEE Trans. on Intelligent Transportation System, June 2006. [18] M. Nieto, L. Salgado, F. Jaureguizar, and J. Cabrera, “Stabilization of Inverse Perspective Mapping Images based on Robust Vanishing Point Estimation”, IEEE Intelligent Vehicles Symposium, IV 2007, Istanbul, Turkey, pp. 315-320, Jun. 2007. [19] M. Nieto, and L. Salgado, “Real-time Vanishing Point Estimation in Road sequences using Adaptive Steerable Filter Banks”, Int. Conf. on Advanced Concepts for Intelligent Vision Systems, ACIVS 2007, Delft, Netherlands, vol. LNCS 4678, pp. 840-848, Aug. 2007. [20] Y. Chen, M. Das, and D. Bajpai, “Vehicle tracking and distance estimation based on multiple image features,” in Proc. IEEE 4th Canadian Conf. on Computer and Robot Vision, Montreal, Canada, pp. 371–378, May, 2000. [21] B. Zheng, B. Tian, J. Duan, and D. Gao, “Automatic Detection Technique of Preceding Lane and Vehicle,” in Proc IEEE Int. Conference on Automation and Logistics, Qingdao, China, September, 2008. [22] J. C. McCall, and M. M. Trivedi, ”An Integrated, Robust Approach to Lane Marking Detection and Lane Tracking,” IEEE Intelligent Vehicles Symposium, University of Parma Parma, Italy June,2004. [23] T. Xiong, and C. Debrunner,” Stochastic Car Tracking With Line and Color Based Features,” IEEE Trans. on Intelligent Transportation Systems, vol. 5, no. 4, Dec. 2004. [24] L. Van and C. A. Groen, “Vehicle detection with a mobile camera: spotting midrange, distant, and passing cars,” Robotics and Automation, vol.12, pp.37-43, 2005. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/41403 | - |
| dc.description.abstract | 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。 | zh_TW |
| dc.description.abstract | 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. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-15T00:18:18Z (GMT). No. of bitstreams: 1 ntu-98-R96525030-1.pdf: 1373830 bytes, checksum: 313956b96945d531635671e70af1ba7e (MD5) Previous issue date: 2009 | en |
| dc.description.tableofcontents | 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 | |
| dc.language.iso | en | |
| dc.subject | 相機校正 | zh_TW |
| dc.subject | 行車輔助系統 | zh_TW |
| dc.subject | 車道偵測 | zh_TW |
| dc.subject | 車輛偵測 | zh_TW |
| dc.subject | Car Detection | en |
| dc.subject | Camera Calibration | en |
| dc.subject | DAS | en |
| dc.subject | Lane Detection | en |
| dc.title | 即時相機校正之前車偵測 | zh_TW |
| dc.title | Front Car Detection with Real-Time Camera Calibration | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 97-1 | |
| dc.description.degree | 碩士 | |
| dc.contributor.coadvisor | 張瑞益 | |
| dc.contributor.oralexamcommittee | 李明穗,王家輝 | |
| dc.subject.keyword | 行車輔助系統,車道偵測,車輛偵測,相機校正, | zh_TW |
| dc.subject.keyword | DAS,Lane Detection,Car Detection,Camera Calibration, | en |
| dc.relation.page | 62 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2009-04-06 | |
| dc.contributor.author-college | 工學院 | zh_TW |
| dc.contributor.author-dept | 工程科學及海洋工程學研究所 | zh_TW |
| 顯示於系所單位： | 工程科學及海洋工程學系 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-98-1.pdf 未授權公開取用 | 1.34 MB | Adobe PDF |
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