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.
翻译:交通现场分析对智能交通管理和自主车辆等新兴技术十分重要,但这种分析也构成潜在的隐私威胁,例如,一个能够识别车辆牌照的系统可以构建相应车辆车主的行为模式,并将其用于各种非法目的。在本文件中,我们提出了一个系统,既能进行交通现场分析,同时又能保护车牌的隐私。该系统基于一个多任务模型,其潜在空间根据具体特征对分析任务和私人信息所传递的信息量有选择地压缩。在城市数据集上进行的实验也说明了拟议方法的有效性,我们还提供了车牌说明。