Person Re-Identification Based on Spatial Feature Learning and Multi-Granularity Feature Fusion; [基于空间特征学习与多粒度特征融合的行人重识别]

In view of the weak ability of the convolutional neural networks to explicitly learn spatial invariance and the probabilistic loss of discriminative features caused by occlusion and background interference in pedestrian re-identification tasks, a person re-identification method combining spatial feature learning and multi-granularity feature fusion was proposed. First, an attention spatial transformation network (A-STN) is proposed to learn spatial features and solve the problem of misalignment of pedestrian spatial features. Then the network was divided into a global branch, a local coarse-grained fusion branch, and a local fine-grained fusion branch to extract pedestrian global features, coarse-grained fusion features, and fine-grained fusion features, respectively. Among them, the global branch enriches the global features by fusing different pooling features. The local coarse-grained fusion branch uses an overlay pooling to enhance each local feature while learning the correlation relationship between multi-granularity features. The local fine-grained fusion branch uses a differential pooling to obtain the differential features that were fused with global features to learn the relationship between pedestrian local features and pedestrian global features. Finally, the proposed method was compared on three public datasets: Market1501, DukeMTMC-ReID and CUHK03. The experimental results were better than those of the comparative methods, which verifies the effectiveness of the proposed method. © 2023, Shanghai Jiao Tong University.