外文資料翻譯---車牌識別-其他專業(yè)-資料下載頁
2025-01-19 10:24本頁面
【導(dǎo)讀】商業(yè)運作系統(tǒng)的幾個重要組成部分之一。然而許多類似系統(tǒng)需要復(fù)雜的視頻采集硬。的視頻信號分辨，具備較高的識別率而且不需要昂貴的硬件。我們的提出的方法將提供給民間基礎(chǔ)設(shè)施寶貴的信息，并提。供以各種情境為執(zhí)法對象的信息。車牌識別問題被廣泛認為是與許多系統(tǒng)急待解決的問題之一。下才能正常工作。車輛的身份完全基于附帶的車牌。統(tǒng)和檢索時，該車輛失蹤與當(dāng)時存檔的錄像資料以及時間記號。成像硬件，同時可以用于探索汽車制造商和型號識別。車牌其輸出為1否則為0。由于與車牌識別問題，探測車的第一步，品牌和型號進行識別。我們發(fā)現(xiàn)，即使我們嘗試在104×31的OCR軟件包決議也產(chǎn)生了。向的汽車，車牌裝在他們的保險杠會在圖像中出現(xiàn)道路的圖像。該網(wǎng)站允許用戶輸入檢查汽車號碼檢測是否已通。美國加利福尼亞州要求所有車輛超過三年以上才能通過煙霧檢查每。多種品牌和型號的圖像類，與相同品牌和型號，但不同年份多個查詢在某些情況下，
【正文】 ound features. Had we centered the license plate both vertically and horizontally, cars that have their plates mounted on their bumper would have exposed the road in the image. After collecting these images, we manually assigned make, model, and year labels to 790 of the 1,140 images. We were unable to label the remaining 350 images due to our limited familiarity with those cars. We often made use of the California Department of Motor Vehicles’ web site to determine the makes and models of cars with which we were not familiar. The web site allows users to enter a license plate or vehicle identification number for the purposes of checking whether or not a car has passed recent smog checks. For each query, the web site returns smog history as well as the car’s make and model description if available. The State of California requires all vehicles older than three years to pass a smog check every two years. Therefore, we were unable to query cars that were three years old or newer and relied on our personal experience to label them. We split the 1,140 labeled images into a query set and a database set. The query set contains 38 images chosen to represent a variety of make and model classes, in some cases with multiple queries of the same make and model but different year in order to capture the variation of model designs over time. We evaluated the performance of each of the recognition methods by finding the best match in the database for each of the query images. SIFT Matching Scale invariant feature transform (SIFT) features recently developed by Lowe [14] are invariant to scale, rotation and even partially invariant to illumination differences, which makes them well suited for object recognition. We applied SIFT matching to the problem of MMR as follows: 1. For each image d in the database and a query image q, perform keypoint localization and descriptor assignment. 2. For each database image d: (a) For each keypoint kq in q find the keypoint kd in d that has the smallest L2 distance to kq and is at least a factor of _ smaller than the distance to the next closest descriptor. If no such kd exists, examine the next kq. (b) Count the number of descriptors n that successfully matched in d. 3. Choose the d that has the largest n and consider that the best match. 5 Results The SIFT matching algorithm described above yielded a recognition rate of % on the query set. Recognition results for some of the queries in the test set are shown in Figure 6. The top 10 matches were all of the same make and model for some of the queries with over 20 similar cars in the database. Most of the queries SIFT matching was not able to classify correctly had 5 or fewer entries similar to it in the database. Based on the results of queries corresponding to makes and models with many examples in the database, it is safe to assume that having more examples per make and model class will increase the recognition rate.
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