Visibility-Guided and Occlusion-Simulated Learning for Robust Person Re-Identification

Occlusion is a critical challenge in person re-identification (ReID), as partial visibility severely degrades feature discriminability and matching reliability. To address this issue, we propose a novel framework termed Visibility-Guided and Occlusion-Simulated Learning (VGOSL) for robust person ReID. The framework consists of two key components: a part-aware visibility modeling (PVM) module and an occlusion box simulation (OBS) module. The PVM module explicitly estimates part-level visibility reliability and adaptively reweights local features to guide global representation learning, enabling the model to emphasize informative regions while suppressing occluded ones. Meanwhile, the OBS module introduces structured occlusion box simulation during training to enhance robustness against realistic obstruction patterns through multi-branch supervision. Extensive experiments on Occluded-DukeMTMC, DukeMTMC-reID, Market-1501, Partial-ReID, and MSMT17 demonstrate that the proposed framework achieves competitive performance under both occluded and holistic settings. The source code has been publicly released on GitHub.