Camera-Incremental Person Re-Identification Via Geometric Representation Evolution

Person re-identification (ReID) is a core technology in large-scale intelligent surveillance systems. In practical deployments, surveillance networks evolve continuously as new cameras are incrementally integrated. As the network expands, the system not only encounters new identities but also captures previously known identities from novel camera views. However, existing LReID studies typically assume disjoint identity spaces across stages, failing to model the cumulative evolution of features induced by such expansion. To bridge this gap, we introduce Camera-Incremental Person Re-Identification (CIReID), a challenging setting that requires continuously updating representations from newly added cameras without access to historical raw data. Unlike traditional closed-set forgetting, the primary challenge in CI-ReID lies in feature space distortion and representation fragmentation across domain shifts. To address this, we propose Camera-Incremental Geometric Representation Evolution (CIGRE). First, we introduce a Dynamic Geometric Alignment (DGA) mechanism, which flexibly expands an Equiangular Tight Frame (ETF) classifier to provide a stable and scalable discriminative geometric skeleton for the evergrowing identity set. Building upon this stable structure, we propose a Cross-Camera Identity Consolidation (CCIC) strategy. By maintaining momentum-updated global prototypes, CCIC progressively aggregates the fragmented identity features revealed under novel views. Furthermore, we establish a new CI-ReID benchmark derived from standard datasets. Extensive experiments demonstrate that CIGRE effectively scales with longterm camera expansion and achieves state-of-the-art performance by strictly maintaining cross-camera consistency. Our code and models will be released.