臺灣博碩士論文加值系統

　　由於各國的社會經濟的快速發展，車輛不斷增加但新闢道路有限，故解決交通擁塞問題刻不容緩，本論文提出一個對於交通影像序列的自動監控系統。此交通監控系統由三部分所組成，首先，我們使用適應性背景更新法和背景差值法來偵測移動物體；然後，卡門濾波器再加以追蹤個別的移動物體；最後，我們利用視覺上的直觀，提出一個新方法將車輛分類。另外，由於車牌辨識在交通監控系統內扮演一個不可或缺的角色，所以本論文也提出以形態學為基礎的方法來定位車牌。車牌辨識系統由三部分構成，首先，利用形態學基礎的方法來抽取具高對比特徵的車牌；接著，如果車牌被切割幾個部分，再利用車牌校正演算法予以校正；最後以車牌中所出現的字元數做車牌確認的工作。實驗結果證明我們所發展的系統具有不錯的穩定性與實用性。

This thesis develops an automatic traffic surveillance system for tracking and classification vehicles in traffic video sequences. First, we utilize an adaptive background update method and background difference to detect moving objects. Then, a Kalman filter was used to stabilize tracking and track individual objects. Finally, we propose a novel method to classify vehicles by means of direct percept on visual data. On the other hand, license plate recognition (LPR) is a very important task in the traffic surveillance system. This thesis also presents a morphology-based method for extracting license plates from cluttered images. The LPR system consists of three major components. At the first, a morphology-based method is proposed to extract important contrast features as guides to search the desired license plates. A recovery algorithm is then applied for reconstructing a license plate if the plate is fragmented into several parts. The last step of the proposed method performs license plate verification based on the number of characters appearing in the plate. Experimental results show that the developed system improves the sate-of-the-art work in terms of effectiveness and robustness for classification of vehicles and license plate detection.

摘要 I
ABSTRACT II
誌謝 III
目錄 IV
圖目錄 VI
表目錄 VII
第一章　介紹 1
1.1 研究目的 1
1.2 論文架構 4
第二章　交通自動監控系統 5
2.1 系統構架 5
2.2 擷取影像與建立街道處理區域 7
2.2.1 影像擷取 7
2.2.2 街道處理區域之建立 8
2.3 物件偵測與背景調適 9
2.3.1 影像差值法 9
2.3.2 編號演算法與雜訊濾除 11
2.3.3 背景調適 15
2.4 物件追蹤 16
2.4.1 卡門濾波器（Kalman Filter） 16
2.4.2 卡門濾波器之應用 20
2.5 物件分類 22
2.5.1 模糊平均值分類法(Fuzzy C-Means) 22
2.5.2 直線逼近法 25
2.5.3 最大可能性估測 30
2.6 交通流量 32
第三章　車牌辨識系統 33
3.1 系統架構 33
3.2 特徵抽取 35
3.2.1 前處理 35
3.2.2 形態學運算 36
3.3 車牌切割 39
3.3.1 候選區域抽取 39
3.3.2 車牌候選區域校正 40
3.3.3 車牌傾斜矯正 44
第四章　實驗結果與分析 45
4.1 交通自動監控系統 45
4.1.1 物件追蹤 46
4.1.2 物件分類 48
4.1.3 交通流量 52
4.2 車牌辨識系統 54
4.2.1 正常影像 55
4.2.2 車牌與背景相似的影像 56
4.2.3 傾斜車牌影像 57
4.2.4 較大、較小車牌影像 58
4.2.5 較亮、較暗影像 60
4.2.6 車牌有雜訊的影像 61
4.2.7 有2個車牌的影像 62
4.2.8 失敗的影像 63
第五章　結論與討論 64
參考文獻 65

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