Traffic images are constantly used as a stick to assess traffic conditions. Traffic flow statistical analysis, road condition safety monitoring, vehicle violation detection, accident surveillance, and the driving environment perception of autonomous vehicles are all functionalities that depend on the processing of traffic images. However, traffic images contain privacy-sensitive information related to users, such as license plate numbers, drivers and passengers. Extracting, analyzing and sharing such privacy-sensitive information without any security measures may raise concerns among users about potential privacy violations. This paper proposes a multi-level privacy protection scheme for traffic image extraction based on compressive sensing, which has the advantages of data undersampling, privacy protection and data access control. Detailed compression rate analysis demonstrates that the proposed solution can effectively reduce the transmission load of image information. Security analysis demonstrates that the proposed approach achieves multi-level reconstruction qualities and high-security strength for users with different authorities. Thus, it has a good application prospect in an image-based intelligent traffic management system.

中文翻译：

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一种交通图像提取的多级隐私保护方案

交通图像经常被用作评估交通状况的工具。交通流量统计分析、路况安全监控、车辆违规检测、事故监控、自动驾驶车辆的行驶环境感知等功能都依赖于交通图像的处理。然而，交通图像包含与用户相关的隐私敏感信息，例如车牌号、驾驶员和乘客。在没有任何安全措施的情况下提取、分析和共享此类隐私敏感信息可能会引起用户对潜在隐私侵犯的担忧。本文提出一种基于压缩感知的交通图像提取多级隐私保护方案，具有数据欠采样、隐私保护和数据访问控制等优点。详细的压缩率分析表明，该方案可以有效降低图像信息的传输负载。安全分析表明，该方法为不同权限的用户实现了多级重建质量和高安全强度。因此，它在基于图像的智能交通管理系统中具有良好的应用前景。