关键词: 关键词: 行人重识别, 时空建模, 柔性特征融合, 特征增强, 遮挡

Abstract: To address the challenges of person re-identification (ReID) in complex scenarios such as occlusion, viewpoint variation, and pose changes, this paper proposes a spatio-temporal modeling and hierarchical feature enhancement framework. The proposed method employs a three-stage progressive feature optimization strategy to achieve a synergistic improvement in global consistency and local discriminability. First, after extracting appearance features with the backbone network, a Double Pooling Temporal Attention mechanism is introduced. This mechanism combines global average pooling and temporal average pooling to capture complementary information from sequence features, and performs spatio-temporal dependency modeling through channel–space interaction, thereby highlighting motion-related features and alleviating the problem of local information loss caused by occlusion. Second, to tackle the imbalance in body-part feature distribution, a flexible feature fusion module is designed. This module adaptively aggregates multi-part features through learnable weights, suppressing occlusion-induced noise while enhancing discriminative local representations, resulting in complementary global-local embeddings. Finally, a confidence calibration network based on residual learning is embedded before the classification layer to refine the distribution of identity prediction confidence, which effectively improves cross-camera retrieval accuracy. Comprehensive evaluations on public datasets such as Market-1501 and P-Duke-MTMC demonstrate that the proposed method achieves mAP scores of 93.2% and 86.8%, with Rank-1 accuracies of 97.4% and 95.2%, respectively. The results indicate that the integration of spatio-temporal modeling with hierarchical feature enhancement significantly improves ReID performance under challenging conditions.

Key words: Keywords: Person Re-identification, Spatio-Temporal Modeling, Flexible Feature Fusion, Feature Enhancement, Occlusion