发表机构
The University of Texas at Austin（德克萨斯大学奥斯汀分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出并测试了一种基于智能手机的自动超速执法方法，利用计算机视觉进行速度估计和车辆识别，在巴西和奥斯汀数据集上验证了其可行性，并探讨了法律与实践应用前景。
AI 中文摘要
智能手机摄像头和计算机视觉（CV）在协助公共机构执行交通法规和提升道路安全方面具有显著潜力。本研究设计并测试了一种基于智能手机的自动速度估计和车辆识别（车牌、品牌/型号和颜色识别）方法，通过自动化流程协助执法机构可靠地识别超速者。该计算机视觉代码在来自巴西开源数据集UFPR-ALPR的1800张图像上，准确识别了近一半（46%）的车牌文本。代码测试基于德克萨斯州奥斯汀的手持智能手机视频（n=73）和路边摄像头（n=42）的白天录制内容，颜色检测准确率为60.8%（在所有可能的RGB颜色类别中），车辆品牌/制造商识别准确率为48.6%，车辆品牌和型号识别准确率为16.89%。速度估计（在20%范围内）、车辆品牌（在前3个预测中）和车牌识别（在前10个预测中）的预测准确率分别为16.3%、16.9%和29.7%。本文还阐明了使用智能手机进行执法的法律、技术和实践方面，包括将录制内容用于执法目的的潜在用途，强调需要将基于智能手机的计算机视觉技术的潜力转化为获取交通违规关键信息的实用工具。
英文摘要
Smartphone cameras and computer vision (CV) hold significant promise in assisting public agencies with enforcing traffic laws and enhancing road safety. This work designs and tests a smartphone-based method for automated speed estimation and vehicle identification (license plate, make/model, and color recognition) via an automated pipeline to assist enforcement agencies in reliably identifying speeders. The CV code accurately recognizes nearly half (46%) of the license plates' text on 1,800 images from a Brazil open-source dataset, called UFPR-ALPR. Code tests on daytime recordings from hand-held smartphone videos (n = 73) and roadside cameras (n = 42) in Austin, Texas yield 60.8% accuracy for color detection (among all possible RGB color categories), 48.6% on vehicle make/manufacturer identification, and 16.89% on vehicle make and model identification. Prediction accuracy for speed estimation (within a 20% range), vehicle make (within the top 3 predictions), and license plate recognition (within the top 10 predictions) are 16.3%, 16.9%, and 29.7%, respectively. This paper also illuminates the legal, technological, and practical aspects of using smartphones for enforcement, including the potential use of recordings for enforcement purposes, emphasizing the need to transform the potential of smartphone-based CV technologies into practical tools for vital information on traffic violations.
