研究生: |
歐原良 YUAN-LIANG OU |
---|---|
論文名稱: |
可攜式車牌辨識系統 A Portable License Plate Recognition System |
指導教授: |
王乃堅
Nai-Jian Wang |
口試委員: |
呂學坤
Shyue-Kung Lu 郭景明 Jing-Ming Guo 蔡超人 Chau-Ren Tsai 鍾順平 Shun-Ping Chung |
學位類別: |
碩士 Master |
系所名稱: |
電資學院 - 電機工程系 Department of Electrical Engineering |
論文出版年: | 2012 |
畢業學年度: | 100 |
語文別: | 中文 |
論文頁數: | 66 |
中文關鍵詞: | 字元辨識 、車牌辨識 、樣板比對 |
外文關鍵詞: | Characters recognition, License plate recognition, Template matching |
相關次數: | 點閱:401 下載:9 |
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現今許多車牌辨識方面的研究都是使用固定式攝影機與個人電腦來達成,然而這種系統大多會設下許多限制條件,如:固定背景、特定拍攝角度和畫面中車輛數,因此在我們研究裡會盡可能的減少辨識環境的限制。
所以本論文將探討一個嵌入式系統,畫面中能同時存在多輛汽機車,且容許較大的拍攝角度,並使用較低運算複雜度的演算法。此系統共包含四個區塊:車牌定位、歪斜車牌矯正、字元切割、字元辨識,一開始我們先利用車牌對比明顯的特徵來找出畫面中可能的車牌,之後再利用影像幾何轉換來校正車牌,接著使用投影法切出車牌上的字元,以較具有適應性的樣板比對來完成車牌的辨識。
最後將我們提出之演算法實現於SIMIS BA-8嵌入式平台上,此系統的優點是有多車牌辨識能力、方便攜帶且擁有大角度拍攝辨識的能力。實驗結果顯示此架構有相當靈活性且能夠達到每秒八張以上的即時辨識速度,並在大部份的環境中都有不錯之辨識率。
Nowadays, most researches on the license plate recognition system are implemented by computers with fixed cameras. However, most of them operate under restricted conditions, such as static background, fixed shooting angle, and limited number of cars. In this research, as few constraints as possible on the working environment are considered.
Our study focuses on the development of a real-time multiple vehicles license plate recognition system on embedded devices with algorithms in low computational complexity and camera shooting angle in large range. It consists of four processing modules: plate localization, skew correction, characters segmentation, and character recognition. By the characteristics of high-contrast of plate image, the license plates are first localized. After detecting the skew lines, the skew plate images are corrected by geometric transformation. Then, applying projection method, the characters on each plate are segmented. Finally, a recognition algorithm by an adaptive template matching is applied to identify the characters on the license plate.
The proposed algorithms are implemented on SIMIS BA-8 board. The major advantages of our system are that it accomplishes a multiple license plate detection and recognition system and it is a portable real-time device with large range in shooting angle.
Our algorithm was tested with video streams in different shooting angles, distances, complex background, and variant illumination. The experimental results show that our proposed architecture is a flexible system. It can operate in real-time at a frame rate of 8 fps with good recognition rate in most circumstances.
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