ReID-FSAI: Person Re-Identification Network Fused With Semantic and Attribute Information

Personal belongings information (e.g., backpacks and reticules) and attribute descriptions (e.g., gender and age) provide critical discriminative cues for person re-identification (Re-ID) tasks. However, existing Re-ID algorithms leveraging additional semantic models often fail to accurately recognize personal belongings and suffer from noisy attribute predictions derived from global or local features, as they inadequately exploit attribute correlations. To address these challenges, we propose a novel person re-identification network, ReID-FSAI, which fuses personal belongings information and attribute descriptions from isolated semantic regions. ReID-FSAI integrates personal belongings areas identified through feature clustering with semantic parsing results from an auxiliary semantic model. By treating the generated semantic regions as body labels, our network refines global features into precise semantic features and accurately predicts attribute information from these regions. Furthermore, ReID-FSAI employs a reweighting model to enhance the confidence in specific attributes, improving attribute prediction accuracy. By combining predictions of attributes and personal belongings with global features, our approach significantly improves the representation ability of pedestrians. Experimental evaluations on the Market-1501 and DukeMTMC-reID datasets demonstrate that ReID-FSAI achieves superior performance in both person re-ID and attribute prediction, surpassing state-of-the-art methods.