Abstract: Person re-identification(ReID) has significant application value in cross-camera retrieval scenarios,but deep models often face a significant domain shift problem in real-world deployments.This means that a model trained well on the source domain dataset experiences a sharp performance drop when transferred to a new target camera network.Existing unsupervised domain adaptation methods typically rely on large amounts of unlabeled target domain data for offline clustering and self-training.However,this prerequisite is often difficult to meet in situations involving temporary deployments,privacy constraints,or difficulty in collecting target data in advance.To address this issue,this paper proposes a deep person re-identification adaptation framework based on a small amount of target data.Starting with a pre-trained model in the source domain,it freezes the backbone parameters and introduces only a lightweight,efficient adaptation module for target domain calibration.Simultaneously,it employs a prototype-based stable few-sample decision-making approach,aggregating a small number of labeled target samples into class centers to reduce few-sample noise.Furthermore,it combines prototype classification loss,ranking constraints,and distillation regularization for optimization,balancing target domain adaptability and feature stability.In cross-dataset migration experiments on Market-1501 and DukeMTMC-reID,the proposed method achieves significant improvements in both migration directions:79.68% mAP and 93.10% Rank-1 on Market→Duke,and 76.07% mAP and 93.79% Rank-1 on Duke→Market,with a continuous improvement trend in incremental adaptation rounds.This method can achieve effective and iterative cross-domain adaptation without relying on large-scale target data.

Key words: Person re-identification, Deep learning, Unsupervised domain, Limited target data, Few-sample decision-making