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.

翻译：行人重识别（ReID）通过关联复杂环境中多个摄像头下的身份信息，在智能监控系统中发挥着关键作用。然而，ReID面临着外观变化、域偏移以及标注数据有限等重大挑战。本论文提出了三种先进方法，分别在有监督、无监督域自适应（UDA）以及完全无监督的设置下提升ReID性能。首先，SCM-ReID将监督对比学习与混合损失优化（分类损失、中心损失、三元组损失及质心-三元组损失）相结合，提升了判别性特征表示能力，并在Market-1501和CUHK03数据集上取得了最先进的准确率。其次，针对UDA场景，IQAGA与DAPRH方法结合了基于GAN的图像增强、域不变映射以及伪标签细化，以缓解域差异并增强跨域泛化能力。实验表明，在具有挑战性的迁移场景中，这些方法相较于基线方法取得了显著提升，mAP和Rank-1指标最高可提升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。该方法通过将全局与局部注意力机制与相机身份约束相结合，在大规模基准数据集上显著超越了现有的无监督方法。在CUHK03、Market-1501、DukeMTMC-reID和MSMT17数据集上的综合评估验证了所提方法的有效性。这些贡献通过解决特征学习、域自适应和标签噪声处理等关键局限性，推动了ReID研究的发展，为在实际监控系统中的鲁棒部署铺平了道路。