UD-Gaussian: Uncertainty-Driven Gaussian Modeling for Occluded Person Re-identification

Occluded person re-identification aims to address the identification challenges posed by pedestrians obscured by other individuals or objects. Existing methods often rely on incorporating pose or semantic information to improve model performance under occlusion. However, such information often depends on external models with inevitably cross-domain gaps, whose stability is limited in complex occlusion environments and prone to false results. In this paper, we propose a Transformer-based uncertainty-driven Gaussian model, termed as UD-Gaussian. Firstly, to enrich the detailed features of pedestrian images, a high-frequency enhancement module is introduced. The high-frequency components of the pedestrian image are extracted by Discrete Haar Wavelet Transform, and Top-K high-frequency patches are extracted to construct a graph Laplacian matrix to achieve high-frequency graph attention, which is fused with features learned from self-attention to enhance the high-frequency feature representation. Given the uncertainty in pedestrian feature learning induced by occlusion makes it challenging to obtain reliable and stable pedestrian features, we propose a probability distribution learning module. This module establishes a memory bank to build Gaussian distributions for each pedestrian identity and the entropy is introduced as a loss function to encourage the model to generate more deterministic and relatively independent probability distributions, thereby enhancing the discriminative ability of the model across different pedestrian identities. The high-frequency enhancement module provides a solid foundation for the probability distribution learning module, alleviating uncertainty caused by pedestrian images themselves. Experimental results on occluded and holistic person re-identification datasets demonstrate the superiority of the proposed method.