论文标题
车牌隐私在交通场景的合作视觉分析中
License Plate Privacy in Collaborative Visual Analysis of Traffic Scenes
论文作者
论文摘要
交通现场分析对于智能交通管理和自动驾驶汽车等新兴技术很重要。但是，这种分析也构成了潜在的隐私威胁。例如，可以识别车牌的系统可以构建相应车辆所有者的行为模式，并将其用于各种非法目的。在本文中，我们介绍了一个系统，该系统可以实现交通现场分析，同时保留车牌隐私。该系统基于多任务模型，该模型的潜在空间被选择性地压缩，具体取决于特定功能携带的有关分析任务和私人信息的信息。通过在CityScapes数据集上的实验说明了所提出的方法的有效性，我们还提供了车牌注释。
Traffic scene analysis is important for emerging technologies such as smart traffic management and autonomous vehicles. However, such analysis also poses potential privacy threats. For example, a system that can recognize license plates may construct patterns of behavior of the corresponding vehicles' owners and use that for various illegal purposes. In this paper we present a system that enables traffic scene analysis while at the same time preserving license plate privacy. The system is based on a multi-task model whose latent space is selectively compressed depending on the amount of information the specific features carry about analysis tasks and private information. Effectiveness of the proposed method is illustrated by experiments on the Cityscapes dataset, for which we also provide license plate annotations.
