Privacy-Preserving Replay and Adaptive Relation Distillation for Camera Incremental Person Re-Identification

Zexian Yang, Dayan Wu, Wanqian Zhang, Jingzi Gu, Zheng Lin, Weiping Wang

Abstract: Traditional person re-identification (ReID) methods trained on static data are ill-suited to real-world dynamic surveillance systems. Recently, a more desirable setting "Camera Incremental Person ReID (CIPR)", has been proposed to continually adapt to new cameras and accumulate knowledge. However, prior work on relation distillation heavily constrains intra-class relations for all identities, while under-exploring the credibility of different identities in knowledge transfer. Besides, their rehearsal-free setting sidesteps privacy concerns but compromises performance. In this paper, we present a novel framework, P 2 -ARD, designed specifically for CIPR. Firstly, we propose an innovative Adaptive Relation Distillation loss that automatically selects more crucial identities for distillation. Additionally, we introduce the privacy-preserving replay scheme to effectively retain semantic information while ensuring the privacy of the identity. Finally, we incorporate a cycle-consistent correlation method to address the class overlap issue in CIPR. Extensive experiments demonstrate our method outperforming the state-of-the-art.