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dc.contributor.authorCheng, Chao-Kaien_US
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dc.description.abstract本論文提出車輛特徵擷取方法,可擷取出車輛的三種特徵,並且透過個別之特徵值,成功驗證出已偵測之複數台車輛。提出之特徵擷取方法包含車燈特徵擷取、車牌特徵擷取以及車色特徵擷取。首先為車燈特徵擷取,利用色彩空間的分布特性,限制色值的區間,可成功擷取出車燈,並計算其密度作為特徵值;接著為車牌特徵擷取方面,提出創新的方法如前處理縮小範圍、搜尋範圍決定(Search Area Decision)、區域邊緣加總(Local Edge Quantity)與Refinement,搭配垂直投影(Vertical Projection)、水平投影(Horizontal Projection)等技術即可準確找出移動中車輛之車牌定位,並將特徵值定為車牌中心座標;車色特徵擷取部分,在特定範圍內求出平均YCbCr色值,作為車輛顏色之特徵值。最後,將三種特徵值,使用相對誤差距離(Relative Error Distance)來比對,可成功辨識出不同之車輛。我們針對演算法設計硬體架構,並完成實現,使用TSMC 0.18μm製程技術,處理640×480@30fps的影像,工作頻率為55.50MHz,可達到即時系統的需求。zh_TW
dc.description.abstractIn this thesis, an algorithm with three features extraction for intelligent vehicles is proposed. By utilizing each feature value, the detected vehicles can be successfully verified. The algorithm is proposed for extracting features, including the vehicle light, the location of the license plate, and the vehicle color. First of all, for the vehicle light extraction, the distribution of the YCbCr color model is used to limit chrominance values. After that, not only the vehicle light can be extracted, but the density is also calculated as one feature value. Secondly, the license plate of the moving vehicle can be efficiently localized by the license plate extraction method which consists of Search Area Decision, Local Edge Quantity, and Refinement. Thirdly, color feature value is proposed by calculating average YCbCr values in the area which the light and the plate part were eliminated. In the end, the Relative Error Distance is calculated among three feature values for vehicle matching. The TSMC 0.18μm technology is used to designed the presented algorithm with gate count 390k. The operation frequency was 55.50MHz for real-time system with image size of 640×480@30fps.en_US
dc.description.tableofcontents目錄 i 圖目錄 iv 表目錄 viii 第 一 章 緒論 1 1.1 研究動機 1 1.2 研究目的 3 1.3 論文組織 3 第 二 章 文獻探討 4 2.1 車燈偵測 4 2.2 車牌定位 7 2.2.1 移動中車輛影像 7 2.2.2 靜止中車輛影像 9 2.3 車輛顏色 10 2.4 車輛比對 11 第 三 章 研究方法 12 3.1 演算法架構 12 3.2 車燈偵測(Vehicle Light Detection) 14 3.2.1 色值篩選 14 3.2.2 車燈密度(Light Density) 16 3.3 車牌定位(License Plate Localization) 17 3.3.1 前處理:縮小處理範圍 18 3.3.2 邊緣偵測(Edge Detection) 21 3.3.3 決定搜尋範圍(Searching Area Decision) 27 3.3.4 區域邊緣加總(Local Edge Quantity) 28 3.3.5 垂直與水平投影(Vertical & Horizontal Projection) 31 3.3.6 Refinement 34 3.3.7 尋找車牌位置 35 3.4 車輛顏色擷取(Vehicle Color Extraction) 37 3.5 車輛比對(Vehicle Matching) 38 第 四 章 硬體架構設計與實作 40 4.1 硬體架構 40 4.2 Sobel邊緣濾波器 41 4.2.1 系統硬體架構 41 4.2.2 資料編排(Data Arrangement) 42 4.2.3 處理程序(Operation Procedure) 43 4.3 搜尋範圍決定 45 4.3.1 系統硬體架構 45 4.3.2 記憶體規格 46 4.4 區域邊緣加總 47 4.4.1 系統硬體架構 47 4.4.2 資料編排 47 4.4.3 處理程序 48 4.5 垂直投影 50 4.5.1 系統硬體架構 50 4.5.2 大小比較單元 52 4.6 水平投影 52 4.7 Refinement 53 4.8 管線化架構 54 第 五 章 實驗結果與討論 56 5.1 車燈偵測 56 5.1.1 正常情形 56 5.1.2 特殊情形 58 5.1.3 連續影像討論 59 5.2 車牌定位 63 5.2.1 正常情形 63 5.2.2 特殊情形 68 5.2.3 連續影像探討 71 5.3 車輛比對 74 5.4 演算法程式效能分析 76 5.5 硬體實作結果 78 5.5.1 晶片規格 78 第 六 章 結論與未來工作 80 6.1 結論 80 6.2 未來工作 80 參考文獻 81zh_TW
dc.subjectFeature Extractionen_US
dc.subjectLicense Plate Localizationen_US
dc.subjectLicense Plate Detectionen_US
dc.subjectVehicle Light Detectionen_US
dc.titleA Hardware Architecture of Feature Extraction for Intelligent Vehiclesen_US
dc.typeThesis and Dissertationzh_TW
item.openairetypeThesis and Dissertation-
item.fulltextno fulltext-
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