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.

翻译：行人重识别（ReID）的增量学习旨在开发能够通过连续数据流训练的模型，这在实际应用中更具现实意义。然而，现有增量重识别方法存在两个强假设：相机固定，且新出现数据与旧类别不重叠。这在现实中并不成立，因为之前观察到的行人可能重新出现并被新相机捕捉。本文研究了一种未被探索的场景——相机增量行人重识别（CIPR），该场景通过考虑类别重叠问题推进了现有终身行人重识别的发展。具体而言，来自新相机的新数据很可能包含之前见过的身份，且比例未知。此外，出于隐私考虑，新数据缺乏跨相机标注。为应对这些挑战，我们提出了一种新颖框架ExtendOVA。首先，为处理类别重叠问题，我们引入实例级已见身份识别模块，在实例级别发现之前见过的身份。然后，我们提出一种筛选高置信度身份候选的准则，并设计早期学习正则化项以修正伪标签中的噪声问题。此外，为弥补先前数据的缺失，我们借助原型记忆库创建替代特征，并通过跨相机蒸馏损失进一步保留相机间关系。在多个基准上的综合实验结果表明，ExtendOVA以显著优势超越现有最先进方法。