Automatic Detection and Recognition of Car Plates Based on Cascade Classifier

Noor M. Hashem, H. K. Abbas
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Abstract

The study consists of video clips of all cars parked in the selected area. The studied camera height is1.5 m, and the video clips are 18video clips. Images are extracted from the video clip to be used for training data for the cascade method. Cascade classification is used to detect license plates after the training step. Viola-jones algorithm was applied to the output of the cascade data for camera height (1.5m). The accuracy was calculated for all data with different weather conditions and local time recoding in two ways. The first used the detection of the car plate based on the video clip, and the accuracy was 100%. The second is using the clipped images stored in the positive file, based on the training file (XML file), where the accuracy was 99.8%.
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基于级联分类器的车牌自动检测与识别
这项研究包括所有停在选定区域的汽车的视频剪辑。所研究的摄像机高度为1.5 m,视频片段为18个视频片段。从视频片段中提取图像,用于级联方法的训练数据。在训练步骤完成后,采用级联分类对车牌进行检测。对相机高度(1.5m)级联数据的输出采用Viola-jones算法。以两种方式计算了不同天气条件下的所有数据和当地时间重新编码的精度。第一种是基于视频片段对车牌进行检测,准确率为100%。第二种方法是基于训练文件(XML文件)使用存储在正文件中的剪切图像,其准确率为99.8%。
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发文量
67
审稿时长
18 weeks
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