Abstract
City-scale person re-identification across distributed cameras must handle severe appearance changes from viewpoint, occlusion, and domain shift while complying with data protection rules that prevent sharing raw imagery. We introduce CityGuard, a topology-aware transformer for privacy-preserving identity retrieval in decentralized surveillance. The framework integrates three components. A dispersion-adaptive metric learner adjusts instance-level margins according to feature spread, increasing intra-class compactness. Spatially conditioned attention injects coarse geometry, such as GPS or deployment floor plans, into graph-based self-attention to enable projectively consistent cross-view alignment using only coarse geometric priors without requiring survey-grade calibration. Differentially private embedding maps are coupled with compact approximate indexes to support secure and cost-efficient deployment. Together these designs produce descriptors robust to viewpoint variation, occlusion, and domain shifts, and they enable a tunable balance between privacy and utility under rigorous differential-privacy accounting. Experiments on Market-1501 and additional public benchmarks, complemented by database-scale retrieval studies, show consistent gains in retrieval precision and query throughput over strong baselines, confirming the practicality of the framework for privacy-critical urban identity matching.
Abstract (translated)
跨分布式摄像头的城市级人员再识别系统必须处理由于视角、遮挡和领域偏移造成的严重外观变化，同时还要遵守数据保护规则，防止分享原始图像。我们引入了CityGuard，这是一种拓扑感知变换器，用于在分散监控中实现隐私保护的身份检索。该框架整合了三个组成部分： 1. **自适应散度测度学习器**：根据特征分布调整实例级别的边界值，增加同一类别的紧密性。 2. **空间条件注意机制**：将粗略几何信息（如GPS或部署楼层图）注入基于图的自我注意中，以利用仅需粗糙几何先验的知识实现跨视角的一致对齐，而无需进行高精度校准。 3. **差分隐私嵌入映射和紧凑近似索引结合**：支持安全且成本效益高的部署。 这些设计共同生成了能够在视角变化、遮挡以及领域偏移下保持鲁棒性的描述符，并能够调整隐私与实用性的平衡，同时在严格的差分隐私计算中表现出色。实验表明，在Market-1501和其他公开基准测试上，该框架的检索精度和查询吞吐量均超越了强大的基线系统，证实了其在关键隐私保护的城市身份匹配场景中的实用性。
URL
https://arxiv.org/abs/2602.18047
