標題: 基於串聯式剔除機制來減少視訊中時空搜尋空間的即時車牌辨識
Real-time License Plate Recognition based on Cascaded Rejection Mechanisms to Reduce Spatio-temporal Search Space in Video Sequences
作者: 王舜正
Shen-Zheng Wang
李錫堅
Hsi-Jian Lee
資訊科學與工程研究所
關鍵字: 車牌辨識;時空搜尋空間減少;License Plate Recognition;Spatiotemporal Search Space Reduction
公開日期: 2007
摘要: 在監控應用中,減少搜索空間(SSR)是發展高效率演算法的一項重要關鍵。在接受視訊的車牌辨識系統研究中,我們整合了空間上和時間上的減少搜尋空間技術。然而由於越來越多特徵需量測,計算量可能會顯著地增加。當考量到大多數輸入樣式都是非目標樣本(不是要認出的物件)的這個事實,似乎盡快剔除大量的非目標樣本是相當有效益的。因此我們提出了一個基於串聯式剔除架構來減少視訊中時空搜尋空間,同時確保系統效能的即時車牌辨識。甚至為了在複雜的環境下正確地擷取出車牌,我們首先提出兩個物件表示法:簡潔車牌區域與重複區域。簡潔車牌區域定義為車牌字元上下界內所包含的區域,此區域可以在第一個步驟中擷取出來,以避免額外的字元上下界偵測程序。我們提出的方法就是由減少空間上的搜尋空間開始,其中包含了三個模組:單次掃描的簡潔車牌區域偵測、雙階層的車牌字元切割和適應性的機器學習。經由可便利計算的特徵,如垂直梯度值和擴充Haar-like 特徵值,這些模組擷取出簡潔車牌或字元的候選區域並驗證之。再者,我們提出要剔除在視訊中重複出現的相同外觀區域,這些區域通常包含有停止的車輛或是固定的背景,且應可剔除以避免被重複分類。為了效能考量,重複樣式只會在車牌候選區域內偵測,在此稱之為時空關係上的減少搜尋空間。且該重複樣式比對是基於區塊為基礎的機制來設計,透過計算正切距離來克服位置、大小、旋轉或是亮度的變異。在我們的實驗中,經由時空關係上的減少搜尋空間,可減少87.9% 的搜尋空間;該車牌辨識系統在Intel P-IV 3-GHz 的個人電腦上,每秒可以處理 38 張 640x480 解析度的影像。
In surveillance applications, search space reduction (SSR) is an essential element to efficient algorithms. In this study, spatial and temporal SSRs are integrated for license plate recognition (LPR) in video sequences. However, as more features are measured, the computational load may increase significantly. When regard to the fact that most input patterns are negatives, it is apparently efficient to reject a majority of negatives as soon as possible. Therefore, we propose a realtime LPR based on a cascaded rejection framework to reduce spatiotemporal search space rapidly, while ensuring that the performance is high. To extract plates accurately even in complicated situations, two representations, compact plate regions and repeated regions, are first presented. Compact plate regions, which bound the top and bottom of plate characters, could be extracted in the first stage to avoid the use of additional removal procedures. Our method started from spatial SSR by algorithms of one-pass compact plate extraction, bi-level plate character segmentation, and adaptive machine learning. Region candidates of compact plates or plate characters are extracted and verified by these algorithms performed on effectively calculated features, such as vertical gradients and extended Haar-like features. Moreover, we proposed to exclude repeated patterns with the similar appearances in the same location of consecutive frames, which usually include stopped vehicles or regular backgrounds and could be excluded from repeated classification. For efficiency, repeated patterns were detected only on the plate candidates, named spatiotemporal SSR, based on a block-based mechanism by estimating the tangent distance, which is invariant to the variations in positions, sizes, rotations, or brightness. In our experiments, the search space could be reduced up to 87.9% by the spatiotemporal SSR; the LPR system can recognize plates over 38 frames per second with a resolution of 640 x 480 pixels on a 3-GHz Intel P-IV PC.
URI: http://140.113.39.130/cdrfb3/record/nctu/#GT008917802
http://hdl.handle.net/11536/77635
Appears in Collections:Thesis


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