用单帧视频识别车牌，速度提升三倍且精度相当。
Efficient License Plate Recognition in Videos Using Visual Rhythm and Accumulative Line Analysis
- 基于视觉节奏或累积线分析，精准定位每辆车的单帧图像
- 处理速度达传统方法三倍，准确率与逐帧分析相当
- 适合实时交通监控系统，降低算力需求
基于视频的自动车牌识别（ALPR）需从视频中提取车辆牌照文本信息。传统系统依赖高性能计算资源，通常使用多帧进行识别，导致计算开销大。本文提出两种方法，仅需每辆车一帧即可完成车牌检测与字符识别，显著降低计算负担。第一种方法利用视觉节奏（VR）生成时空图像，第二种采用基于单行视频处理的累积线分析（ALA）算法，支持实时运行。两方法均结合YOLO进行车牌定位，使用卷积神经网络（CNN）实现光学字符识别（OCR）。在真实视频数据上的实验表明，所提方法性能接近传统逐帧方法，但处理速度提升三倍。
原文摘要 · Abstract (English)
Video-based Automatic License Plate Recognition (ALPR) involves extracting vehicle license plate text information from video captures. Traditional systems typically rely heavily on high-end computing resources and utilize multiple frames to recognize license plates, leading to increased computational overhead. In this paper, we propose two methods capable of efficiently extracting exactly one frame per vehicle and recognizing its license plate characters from this single image, thus significantly reducing computational demands. The first method uses Visual Rhythm (VR) to generate time-spatial images from videos, while the second employs Accumulative Line Analysis (ALA), a novel algorithm based on single-line video processing for real-time operation. Both methods leverage YOLO for license plate detection within the frame and a Convolutional Neural Network (CNN) for Optical Character Recognition (OCR) to extract textual information. Experiments on real videos demonstrate that the proposed methods achieve results comparable to traditional frame-by-frame approaches, with processing speeds three times faster.
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