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DC 欄位 | 值 | 語言 |
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dc.contributor.advisor | 陳少傑(Sao-Jie Chen) | |
dc.contributor.author | Chen-Min Lin | en |
dc.contributor.author | 林振民 | zh_TW |
dc.date.accessioned | 2021-06-08T05:16:24Z | - |
dc.date.copyright | 2006-01-27 | |
dc.date.issued | 2006 | |
dc.date.submitted | 2006-01-25 | |
dc.identifier.citation | [1] F. M. Alzahrani and T. Chen, “A Real-Time Edge Detector: Algorithm and VLSI Architecture,” Real-Time Imaging, Volume 3 no. 5, pp. 363-378, Oct. 1997.
[2] F. Russo and A. Lazzari, “Color Edge Detection in Presence of Gaussian Noise Using Nonlinear Prefiltering,” IEEE Transactions on Instrumentation and Measurement, Volume 54, Issue 1, pp. 352-358, Feb. 2005. [3] J. Canny, “A Computational Approach to Edge Detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, volume 8, Issue 6, pp. 679-698, November 1986. [4] Y. U. Yim and S.Y. Oh, “Three-Feature based Automatic Lane Detection Algorithm (TFALDA) for Autonomous Driving,” IEEE Transactions on Intelligent Transportation Systems, Volume 4, Issue 4, pp. 219-225, Dec. 2003. [5] A. Broggi, M. Bertozzi, A. Fascioli, C. G. Lo Bianco, and A. Piazzi, “Visual Perception of Obstacles and Vehicles for Platooning,” IEEE Transactions on Intelligent Transportation Systems, Volume 01, Issue 3, pp. 164 – 176, Sept. 2000. [6] M.B. van Leeuwen and F.C.A. Groen, “Vehicle Detection with a Mobile Camera: Spotting Midrange, Distant, and Passing Cars,” IEEE Robotics and Automation Magazine, Volume 12, Issue 1, pp. 37-43, Mar. 2005. [7] Z. Sun, G. Bebis, and R. Miller, “On-Road Vehicle Detection Using Evolutionary Gabor Filter Optimization,” IEEE Transactions on Intelligent Transportation Systems, Volume 6, Issue 2, pp. 125-137, June 2005. [8] S. Gupte, O. Masoud, R.F.K. Martin ,and N.P. Papanikolopoulos, “Detection and Classification of Vehicles,” IEEE Transactions on Intelligent Transportation Systems, Volume 3, Issue 1, pp. 37- 47, March 2002. [9] S. H. Lai, C. W. Fu,” A Generalized Depth Estimation Algorithm with a Single Image,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 14, Issue 4, pp. 405-411, April 1992. [10] M.B. Ahmad and T.-S. Choi, “A Heuristic Approach for Finding Best Focused Shape,” IEEE Transactions on Circuits and Systems, Volume 15, Issue 4, pp. 566-574, April 2005. [11] M. Subbarao and J.-K. Tyan, “Selecting the Optimal Focus Measure for Autofocusing and Depth-from-Focus,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 20, Issue 8, pp. 864-870, August 1998. [12] L. Fletcher, N. Apostoloff, L. Petersson, and A. Zelinsky, “Vision in and out of Vehicles,” IEEE Transactions on Intelligent Systems and Their Applications, Volume 18, Issue 3, pp. 12–17, May-Jun 2003. [13] N. Shimomura, K. Fujimoto, T. Oki, and H. Muro, “An Algorithm for Distinguishing the Types of Objects on the Road using Laser Radar and Vision,”IEEE Transactions on Intelligent Transportation Systems, Volume 3, Issue 3, pp. 189 – 195, Sept. 2002. [14] Y. Nomura, M. Sagara, H. Naruse; A. Ide, “Simple Calibration Algorithm for High-Distortion Lens Camera,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 14, Issue 11, pp. 1095-1099, Nov. 1992. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/24121 | - |
dc.description.abstract | 嵌入式平台(Embedded platform)是目前很普遍被使用的平台架構,它的低成
本、低耗電特性使之應用層面更為廣泛。而 x86 平台是目前大家所最熟悉的平 台,也因此使用此平台開發軟體的時間會縮短很多。但因現有使用Intel x86 中 央處理器的 x86 平台的大多都很耗電,此論文中所使用之中央處理器是 800MHz 的VIA C3 相對於 Intel 者省電許多,再結合 VIA 開發的嵌入式平台 的機版 EPIA-N 其大小只有12cm×12 cm,突破了以往 x86 平台高耗電與大尺 吋的印象,適合應用在嵌入式系統上。 此論文是利用一個 CMOS 感應器,將擷取到的影像資料,透過影像邊緣偵 測(edge detection)處理後,分別同時交給車道偵測(lane detection)與車輛偵測 (vehicle detection)演算法去做車道與車輛的偵測。給予偵測出之車道與車輛的資 訊,即可利用距離遠近所造成車道在視覺呈現的比例關係推算出與前車的距離。 此架構可應用於煞車系統,當偵測出與前車距離過近時,自動啟動煞車系統, 避免碰撞產生,或是當駕駛過於疲憊時,造成車距過近,提供即時的警告訊息。 | zh_TW |
dc.description.abstract | Embedded platform is popularly used for its low cost and low power characteristics. Using a familiar x86 platform can shorten the software development schedule. But, the system power consumption is very high and the size too large for most of the x86 systems using an Intel x86 CPU. The platform presented in this Thesis adopts a 800MHz VIA C3 CPU and the system board size is 12cm×12cm. Its
low power consumption and very small size make it a good platform for embedded system applications. The proposed embedded platform uses a CMOS sensor to capture the image of a vehicle in front, and transfers it through a USB interface to a vehicle filter application in the embedded platform. After the vehicle filter application transforms this color image to a gray scale image, from which an Absolute Difference Mask (ADM) algorithm [1] is used to extract the image edges for detecting the road lane and vehicle information. For the lane detection, we use a TFALDA algorithm [4]. As to the vehicle detection, we use the vehicle symmetric features to search the existence of a vehicle. When the lane and vehicle information are found, we can use the lane width measured from the vehicle to compute the distance of a front vehicle. For the application side, this platform can be used as a vehicle automatic breaking system, or as a secure-distance keeping or warning system. | en |
dc.description.provenance | Made available in DSpace on 2021-06-08T05:16:24Z (GMT). No. of bitstreams: 1 ntu-95-P92921003-1.pdf: 2532489 bytes, checksum: 4469f0d6ccc590cfa3cdae29ce6271c6 (MD5) Previous issue date: 2006 | en |
dc.description.tableofcontents | ABSTRACT ……………………………………………………………………… i
LIST OF FIGURES …………………………………………………………… v LIST OF TABLES …………………………………………………………… viii 1 INTRODUCTION …………………………………………………………… 1 1.1 Motivation …………………………………………………………… 1 1.2 System Architecture ………………………………………………… 1 1.3 Thesis Organization ………………………………………………… 2 2 IMAGE PROCESSING AND DISTANCE ESTIMATION ALGORITHMS ………… 5 2.1 Edge Detection Algorithms ………………………………………… 5 2.2 Lane Detection Algorithms ………………………………………… 11 2.3 Vehicle Detection Algorithms …………………………………… 16 2.4 Distance Estimation Algorithms ………………………………… 19 3 HARDWARE ARCHITECTURE AND SYSTEM FLOW ………………………… 25 3.1 Hardware Architecture ……………………………………………… 25 3.1.1 System Platform Architecture Design ………………………… 27 3.1.2 CMOS Sensor ………………………………………………………… 28 3.1.3 LCD Display Module ……………………………………………… 28 3.1.4 Platform Power Consumption …………………………………… 29 3.2 Software Architecture ……………………………………………… 33 3.3 System Flow …………………………………………………………… 34 3.4 System Performance ………………………………………………… 35 4 SYSTEM VERIFICATION AND MEASUREMENT …………………………… 37 4.1 System Simulation …………………………………………………… 37 4.2 Implementation ……………………………………………………… 40 4.3 Measurement Result ………………………………………………… 42 5 CONCLUSION ……………………………………………………………… 47 REFERENCE …………………………………………………………………… 49 | |
dc.language.iso | en | |
dc.title | 實現即時車輛偵測與距離估計之嵌入式平台 | zh_TW |
dc.title | Automatic Vehicle Detection and Distance Estimation on an Embedded Platform | en |
dc.type | Thesis | |
dc.date.schoolyear | 94-1 | |
dc.description.degree | 碩士 | |
dc.contributor.coadvisor | 蕭培鏞(Pei-Yung Hsiao) | |
dc.contributor.oralexamcommittee | 李宗演(Trong-Yen Lee),張耀文(Yao-Wen Chang) | |
dc.subject.keyword | 車輛偵測,車道偵測,距離估算, | zh_TW |
dc.subject.keyword | Vehicle detection,Lane detection,Distance estimation, | en |
dc.relation.page | 50 | |
dc.rights.note | 未授權 | |
dc.date.accepted | 2006-01-26 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 電機工程學研究所 | zh_TW |
顯示於系所單位: | 電機工程學系 |
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ntu-95-1.pdf 目前未授權公開取用 | 2.47 MB | Adobe PDF |
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