Prototype-guided Knowledge Propagation with Adaptive Learning for Lifelong Person Re-identification

摘要

Lifelong Person Re-identification (LReID) is essential in dynamic camera networks, which continually adapts to new environments while preserving previously acquired knowledge. Existing LReID techniques often preserve samples from past datasets to maintain old knowledge, potentially leading to privacy risks. While prototype-based methods offer privacy advantages, current approaches primarily focus on adjusting classifiers for image classification tasks, neglecting representation biases between old and new identities in person re-identification. This study introduces a novel Prototype-guided Knowledge Propagation (PKP) method, which mitigates discrepancies in similar identity images between old and new tasks by guiding prototype construction through triplet loss constraints. Additionally, to address disparities between prototypes and the updated feature extractor, an Adaptive Parameter Evolution (APE) strategy is proposed. APE optimizes the integration of the old and new models by assessing the importance of the new tasks, dynamically selecting the most pertinent parameters for updates according to their contribution to the current task. Extensive experiments on the LReID benchmark demonstrate that our approach surpasses state-of-the-art prototype-based LReID methods in terms of mAP and rank-1 accuracy. Code is available at https://github.com/joyner-7/IJCAI2025-PKA.