  Incremental learning for person re-identification (ReID) aims to develop
models that can be trained with a continuous data stream, which is a more
practical setting for real-world applications. However, the existing
incremental ReID methods make two strong assumptions that the cameras are fixed
and the new-emerging data is class-disjoint from previous classes. This is
unrealistic as previously observed pedestrians may re-appear and be captured
again by new cameras. In this paper, we investigate person ReID in an
unexplored scenario named Camera Incremental Person ReID (CIPR), which advances
existing lifelong person ReID by taking into account the class overlap issue.
Specifically, new data collected from new cameras may probably contain an
unknown proportion of identities seen before. This subsequently leads to the
lack of cross-camera annotations for new data due to privacy concerns. To
address these challenges, we propose a novel framework ExtendOVA. First, to
handle the class overlap issue, we introduce an instance-wise seen-class
identification module to discover previously seen identities at the instance
level. Then, we propose a criterion for selecting confident ID-wise candidates
and also devise an early learning regularization term to correct noise issues
in pseudo labels. Furthermore, to compensate for the lack of previous data, we
resort prototypical memory bank to create surrogate features, along with a
cross-camera distillation loss to further retain the inter-camera relationship.
The comprehensive experimental results on multiple benchmarks show that
ExtendOVA significantly outperforms the state-of-the-arts with remarkable
advantages.
