摘要： 现有的无监督行人Re-ID方法通常只关注行人的整体特征导致全局特征偏差以及数据多样性不足而影响识别精度，为解决该问题，本文创新性地提出了一种基于ViT的融合多层次数据增强(MDAM)与特征净化(FP)的无监督行人Re-ID方法(DAFP)。首先，设计了包含几何空间变换、表观特征扰动和遮挡模拟的MDAM，扩展了训练样本多样性，提升模型跨摄像头场景的鲁棒性。此外，构建FP模块，将Transformer输出的局部特征按空间位置划分为上下两部分，并与全局特征进行多视图距离矩阵的自适应加权融合，结合DBSCAN聚类算法生成高质量伪标签，有效缓解了传统方法因依赖单一全局特征而导致的相似行人误聚类问题。最后，通过全局-局部聚类对比损失函数，动态更新全局与局部聚类中心，实现了对行人细粒度特征的增强学习。在Market1501、DukeMTMC-reID和MSMT17三个数据集上的mAP/Rank-1结果分别达到90.5%/96.0%，77.6%/87.6%和64.5%/86.0%，显著超越了最先进的同类方法，验证了本方法的优越性。

Abstract: Existing unsupervised person Re-ID methods focus only on pedestrians’ global features, causing global feature bias and insufficient data diversity that impair recognition accuracy.To address this, this paper proposes an innovative ViT-based method(DAFP) integrating Multi-level Data Augmentation (MDAM) and Feature Purification (FP). Firstly, the MDAM—including geometric spatial transformations, appearance feature perturbations, and occlusion simulation—expands training sample diversity and enhances the model’s cross-camera robustness. Additionally, the FP module divides the local features output by the Transformer into upper and lower parts according to spatial positions, performs adaptive weighted fusion with global features via a multi-view distance matrix, and generates high-quality pseudo-labels with DBSCAN, effectively alleviating similar pedestrian misclustering caused by over-reliance on single global features in traditional methods. Finally, a global-local clustering contrastive loss dynamically updates global and local clustering centers to strengthen fine-grained feature learning. Experimental results on Market1501, DukeMTMC-reID, and MSMT17 show that its mAP/Rank-1 reaches 90.5%/96.0%, 77.6%/87.6%, and 64.5%/86.0%, respectively, significantly surpassing the current state-of-the-art methods and fully verifying the superior performance of this method.