Abstract
The collection and release of street-level recordings as Open Data play a vital role in advancing autonomous driving systems and AI research. However, these datasets pose significant privacy risks, particularly for pedestrians, due to the presence of Personally Identifiable Information (PII) that extends beyond biometric traits such as faces. In this paper, we present cRID, a novel cross-modal framework combining Large Vision-Language Models, Graph Attention Networks, and representation learning to detect textual describable clues of PII and enhance person re-identification (Re-ID). Our approach focuses on identifying and leveraging interpretable features, enabling the detection of semantically meaningful PII beyond low-level appearance cues. We conduct a systematic evaluation of PII presence in person image datasets. Our experiments show improved performance in practical cross-dataset Re-ID scenarios, notably from Market-1501 to CUHK03-np (detected), highlighting the framework's practical utility. Code is available at this https URL.
Abstract (translated)
作为开放数据的一部分，街道级别的记录在推进自动驾驶系统和人工智能研究中发挥着至关重要的作用。然而，这些数据集存在显著的隐私风险，尤其是对行人而言，因为其中包含的个人识别信息（PII）超出了面部等生物特征之外的信息。 本文介绍了cRID，这是一种结合了大型视觉-语言模型、图注意力网络以及表示学习的跨模态框架，用于检测文本描述性线索中的个人识别信息，并增强人员重识别（Re-ID）。我们的方法专注于识别和利用可解释的特性，从而能够发现超越低级外观提示的语义上有意义的PII。我们对人物图像数据集中个人识别信息的存在进行了系统的评估。 实验结果显示，在实际跨数据集人员重识别场景中性能有所提升，特别是从Market-1501到CUHK03-np（检测）的数据迁移上表现尤为突出，这表明了框架的实际应用价值。代码可在上述链接中获得。
URL
https://arxiv.org/abs/2507.01504
