Abstract
Person re-identification (ReID) plays a critical role in intelligent surveillance systems by linking identities across multiple cameras in complex environments. However, ReID faces significant challenges such as appearance variations, domain shifts, and limited labeled data. This dissertation proposes three advanced approaches to enhance ReID performance under supervised, unsupervised domain adaptation (UDA), and fully unsupervised settings. First, SCM-ReID integrates supervised contrastive learning with hybrid loss optimization (classification, center, triplet, and centroid-triplet losses), improving discriminative feature representation and achieving state-of-the-art accuracy on Market-1501 and CUHK03 datasets. Second, for UDA, IQAGA and DAPRH combine GAN-based image augmentation, domain-invariant mapping, and pseudo-label refinement to mitigate domain discrepancies and enhance cross-domain generalization. Experiments demonstrate substantial gains over baseline methods, with mAP and Rank-1 improvements up to 12% in challenging transfer scenarios. Finally, ViTC-UReID leverages Vision Transformer-based feature encoding and camera-aware proxy learning to boost unsupervised ReID. By integrating global and local attention with camera identity constraints, this method significantly outperforms existing unsupervised approaches on large-scale benchmarks. Comprehensive evaluations across CUHK03, Market-1501, DukeMTMC-reID, and MSMT17 confirm the effectiveness of the proposed methods. The contributions advance ReID research by addressing key limitations in feature learning, domain adaptation, and label noise handling, paving the way for robust deployment in real-world surveillance systems.
Abstract (translated)
人员再识别（ReID）在智能监控系统中扮演着关键角色，通过连接复杂环境中多个摄像头的身份信息来实现这一点。然而，ReID面临着诸如外观变化、领域迁移和标记数据有限等重大挑战。本文提出三种先进方法以提升监督学习、无监督领域适应(UDA)以及完全无监督设置下的ReID性能。 首先，SCM-ReID结合了监督对比学习与混合损失优化（包括分类、中心点、三元组及重心-三元组损失），增强了判别性特征表示，并在Market-1501和CUHK03数据集上实现了最先进的准确性。其次，在UDA背景下，IQAGA和DAPRH通过结合基于GAN的图像增强、领域不变映射以及伪标签优化来缓解域差异并提高跨域泛化能力。实验结果表明这些方法在具有挑战性的迁移场景中相比基准方法在mAP和Rank-1指标上分别提高了高达12%。最后，ViTC-UReID利用基于Vision Transformer的特征编码与相机感知代理学习来提升无监督ReID性能。通过整合全局及局部注意力，并结合相机身份约束，该方法在大规模基准测试中显著超越了现有的无监督方法。 对CUHK03、Market-1501、DukeMTMC-reID和MSMT17的数据集进行全面评估后确认了所提出方法的有效性。这些贡献通过解决特征学习、领域适应及标记噪声处理的关键限制，为ReID研究铺平了道路，并为其在实际监控系统的稳健部署开启了新的方向。
URL
https://arxiv.org/abs/2601.01356
