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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
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dc.contributor.advisor | 陳湘鳳(Shana Smith) | |
dc.contributor.author | Cheng-Wei Lee | en |
dc.contributor.author | 李政緯 | zh_TW |
dc.date.accessioned | 2021-06-15T00:28:02Z | - |
dc.date.available | 2013-08-20 | |
dc.date.copyright | 2011-08-20 | |
dc.date.issued | 2011 | |
dc.date.submitted | 2011-08-15 | |
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dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/41698 | - |
dc.description.abstract | 隨著汽車數量與肇事數件遽增,智慧汽車系統逐漸受到重視。交通號誌偵測與辨識系統是智慧汽車系統中相當倚重的子系統,因為它能提醒與提供駕駛道路資訊。本系統含有四個模組:前處理模組、訓練模組、偵測模組和辨識模組。在偵測上,我們利用滑動視窗法在測試影像的任何位置來偵測不同大小的號誌,對於每個視窗採用共變數矩陣表示法作為特徵描述的方法,並判斷是否為是號誌。除此之外,採用Adaboost 演算法和分層偵測器 (cascade detector)的概念來縮減運算處理時間與降低假陽性率(false positive rate);最後利用多類別支持向量機(multi-class Support Vector Machine)進行道路號誌辨識。提出的演算法分別在晴天狀況與四種干擾情形: 遮蔽、褪色、背光和模糊景色下進行測試。根據實驗結果,我們所提出的穩健系統可適用於任何環境且無論在偵測或是辨識交通標誌上,都具有高度準確率。 | zh_TW |
dc.description.abstract | Intelligent vehicle (IV) systems have gathered great importance in recent year. Many driver assistance systems have been developed to improve driving safety. Traffic sign recognition system is an important subsystem of driver assistance system because it can remind the drivers of the road sign information. The proposed system comprises four modules: preprocessing, training, detection and recognition. In detection phase, a sliding window is applied to the test image in different scales. For each sliding window, we compute covariance matrix descriptor for feature extraction, and determine whether it is a sign or not. Moreover, in order to reduce computational time and false positive rate, the detector is built by Adaboost algorithm and the cascaded decision. In recognition phase, we perform the sign identification by using multi- class Support Vector Machine (SVM). The proposed algorithms were tested in sunny conditions and four different noisy outdoor scenes: occluded, faded, backlight and blurred conditions. From the experimental results, the proposed system shown high performance to detect and recognize traffic signs. | en |
dc.description.provenance | Made available in DSpace on 2021-06-15T00:28:02Z (GMT). No. of bitstreams: 1 ntu-100-R98522603-1.pdf: 4161974 bytes, checksum: ac8542d21ad6787c51d892a650bae16d (MD5) Previous issue date: 2011 | en |
dc.description.tableofcontents | 摘要 i
Abstract ii Contents iii List of Tables vi List of Figures vii Chapter 1 Introduction 1 1.1 Background 1 1.2 Challenging of Detection and Recognition 2 1.3 Objectives and Contribution 4 1.4 Thesis Organization 4 Chapter 2 Technical Overview 6 2.1 Literature Review 6 2.1.1 Color-Based Detection Approaches 6 2.1.2 Shape-Based Detection Approaches 9 2.1.3 Hybrid Detection Approaches 11 2.1.4 Classification and Recognition Approaches 13 2.1.5 Color-Based Detection Approaches 18 2.2 Color model 19 2.2.1 RGB color mode 19 2.2.2 HSV color model 20 2.3 Covariance Matrix Descriptor 21 2.4 Histogram of Oriented Gradients 24 2.5 Integral Image 25 2.6 Haar-like Feature 26 2.7 Adaboost algorithm 29 2.8 Support Vector Machines 31 Chapter 3 System Architecture for Traffic Sign Detection and Recognition 33 3.1 System Preview 33 3.2 Preprocessing Module 35 3.2.1 Image Acquisition 35 3.2.2 Image Scaling 40 3.2.3 Color Conversion 40 3.3 Training Module 42 3.3.1 Training Dataset 42 3.3.2 Feature Extraction 44 3.3.3 Detector Training 47 3.3.3 Identifier Training 48 3.4 Detection Module 48 3.4.1 Feature Extraction 49 3.4.2 Candidate Localization 52 3.5 Recognition Module 55 3.5.1 Size Normalization 55 3.5.2 Traffic Sign Identification 55 3.6 Summary 57 Chapter 4 Experimental Results 58 4.1 Performance Evaluation 58 4.1.1 Detection Module 58 4.1.2 Recognition Module 62 4.1.3 Effects of Viewing Angle on Recognition Rate 65 Chapter 5 Conclusions 68 5.1 Conclusions 68 5.2 Future Works 69 References 71 Appendix A Literature comparison 77 | |
dc.language.iso | en | |
dc.title | 應用共變異矩陣表示法之加強型交通號誌偵測與辨識系統 | zh_TW |
dc.title | An Enhanced Traffic Sign Detection and Recognition System Using Covariance Matrix Descriptor | en |
dc.type | Thesis | |
dc.date.schoolyear | 99-1 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 劉正良(Cheng-Liang Liu),李綱(Kang Li) | |
dc.subject.keyword | 交通號誌偵測與辨識,共變數矩陣表示法,Adaboost 演算法,多類別支持向量機, | zh_TW |
dc.subject.keyword | traffic sign detection and recognition,covariance matrix descriptor,Adaboost algorithm,multi-class SVM, | en |
dc.relation.page | 80 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2011-08-15 | |
dc.contributor.author-college | 工學院 | zh_TW |
dc.contributor.author-dept | 機械工程學研究所 | zh_TW |
顯示於系所單位: | 機械工程學系 |
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