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研究生:王柏欣
研究生(外文):Po-Hsin Wang
論文名稱:車牌分析於複雜環境下之道路監控系統
論文名稱(外文):License Plate Analysis for Automated Traffic Surveillance in Complex Environment
指導教授:陳洳瑾張雲龍張雲龍引用關係
指導教授(外文):Ju-Chin ChenWeng-Long Chang
學位類別:碩士
校院名稱:國立高雄應用科技大學
系所名稱:資訊工程系
學門:教育學門
學類:專業科目教育學類
論文種類:學術論文
畢業學年度:100
語文別:中文
論文頁數:31
中文關鍵詞:車牌偵測積分影像弱分類器特徵值
外文關鍵詞:license plate detectionintegral imageweak classifierfeature values
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道路監控工作近期在台灣越來越受重視,而道路上也有越來越多的監視器來監控交通狀況,而監視器拍攝的畫面可能是複雜的環境,要監控拍攝到的汽機車是否是合法駕駛,車牌偵測莫過於最實用的方法,於是搜尋許多相關文獻尋找合適的車牌偵測的方法,並且實作它。
然而複雜環境下需要考慮的因素很多,我們選擇了AdaBoost演算法來實作車牌偵測工作,原因是他是一直訓練,把車牌與非車牌分開來當作訓練資料,學習怎麼樣是車牌,怎麼樣是非車牌,如此受到環境因素的影響就大幅減小了,也可以達到不錯的偵測效果。實作過程也運用到了integral image來加速特徵值運算,使用Haar-like特徵來做特徵運算,實驗結果達到不錯的效果。
In Taiwan, the traffic monitor on the road is more and more attention. And more and more monitors to monitor the traffic on the road. The footage that the monitor shoot may be a complex environment. The goal that the car in the image which the monitor photographed is want to monitor the car is legitimate driving. The license plate detection is the most practical way. So we search more and more related literature to find the fitness license plate detection method and implement it.
However, a lot of factors must to be considered in a complex environment. We chose the AdaBoost algorithm to implement the license plate detection work. As this method has been train the training data separately from the license plate and non-license plate. Learn what is license plate and what is non-license plate, and so that the influence by the environment factors are significantly reduced, and that may achieve nice detection result. Implement process is applied the integral image to accelerate the computation of feature values, and use the Haar-like features to compute the feature values. Finally, the experimental results are achieve nice results.
目錄
摘 要-------------------------------------------------------------------------------------- iv
ABSTRACT----------------------------------------------------------------------------- v
誌謝-------------------------------------------------------------------------------------- vi
目錄-------------------------------------------------------------------------------------- vii
表目錄----------------------------------------------------------------------------------- viii
圖目錄----------------------------------------------------------------------------------- ix
一、緒論-------------------------------------------------------------------------------- 1
1.1 研究背景--------------------------------------------------------------------------- 1
1.2 研究動機--------------------------------------------------------------------------- 1
1.3 研究目的--------------------------------------------------------------------------- 2
1.4 論文架構--------------------------------------------------------------------------- 3
二、 文獻探討------------------------------------------------------------------------- 4
2.1 車牌定位(License-Plate Location)------------------------------------------ 4
2.2 車牌字元切割(Character Extraction) --------------------------------------- 5
2.3 車牌字元辨識(License-Plate Recognition)-------------------------------- 6
2.4 研究目的與方向------------------------------------------------------------------ 7
三、 研究方法及系統流程---------------------------------------------------------- 9
3.1 訓練流程--------------------------------------------------------------------------- 10
3.1.1 訓練資料收集與前處理------------------------------------------------------- 11
3.1.2 特徵擷取------------------------------------------------------------------------- 12
3.1.3 AdaBoost演算法--------------------------------------------------------------- 14
3.2 測試流程--------------------------------------------------------------------------- 17
3.3 重點技術--------------------------------------------------------------------------- 19
四、 分析偵測結果------------------------------------------------------------------- 23
4.1 測試資料收集及前處理--------------------------------------------------------- 23
4.2 測試結果與問題探討----------------------------------------------------------- 23
4.3 結果合併-------------------------------------------------------------------------- 27
五、 結論與未來展望--------------------------------------------------------------- 29
參考文獻 ------------------------------------------------------------------------------ 30
參考文獻
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