In real‐world intelligent transportation systems, accuracy in vehicle license plate detection and recognition is considered quite critical. Many algorithms have been proposed for still images, but their accuracy on actual videos is not satisfactory. This stems from several problematic conditions in videos, such as vehicle motion blur, variety in viewpoints, outliers, and the lack of publicly available video datasets. In this study, we focus on these challenges and propose a license plate detection and recognition scheme for videos based on a temporal matching prior network. Specifically, to improve the robustness of detection and recognition accuracy in the presence of motion blur and outliers, forward and bidirectional matching priors between consecutive frames are properly combined with layer structures specifically designed for plate detection. We also built our own video dataset for the deep training of the proposed network. During network training, we perform data augmentation based on image rotation to increase robustness regarding the various viewpoints in videos.

中文翻译：

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视频中的车牌检测和识别的时间匹配先验网络

在现实世界的智能交通系统中，车牌检测和识别的准确性非常关键。已经提出了许多用于静止图像的算法，但是它们在实际视频上的准确性并不令人满意。这源于视频中的一些问题条件，例如车辆运动模糊，视点变化，离群值以及缺乏公开可用的视频数据集。在这项研究中，我们着眼于这些挑战，并提出了一种基于时间匹配先验网络的视频车牌检测和识别方案。具体地，为了在运动模糊和离群值的存在下提高检测和识别精度的鲁棒性，连续帧之间的前向和双向匹配先验与专门设计用于板检测的层结构适当地结合在一起。我们还建立了自己的视频数据集，用于对建议的网络进行深度培训。在网络训练期间，我们基于图像旋转执行数据增强，以提高有关视频中各种视点的鲁棒性。