MSC-TReID: Multi-Scale Transformer for Cross-Domain Person Re-Identification

Person re-identification (Re-ID) is a crucial task in surveillance and security applications, aiming to match individuals across non-overlapping camera views despite variations in illumination, pose, and occlusion. Traditional deep learning methods, particularly convolutional neural networks (CNNs), struggle with cross-domain generalization due to domain shifts. In this paper, we propose MSC-TReID, a Multi-Scale Transformer-based framework designed to enhance feature robustness and cross-domain adaptability. Our model integrates a hybrid CNN-Transformer backbone to capture both local and global identity features, a multi-scale attention mechanism to focus on discriminative regions, and domain adversarial training to mitigate domain shift. Extensive experiments on Market-1501, DukeMTMC-reID, and MSMT17 datasets demonstrate that MSC-TReID outperforms state-of-the-art Re-ID models, achieving significant improvements in Rank-1 accuracy and mean Average Precision. Additionally, cross-domain evaluation results confirm the model's superior generalization ability.