Interleaved Dynamic Fusion Network for Occluded Person Re-Identification

Most existing occluded person re-identification methods use a part-based approach to extract pedestrian features. The extracted part features are isolated from each other, resulting in insufficient information exchange between part features. To address this issue, we propose an interleaved dynamic fusion network (IDFNet) for occluded person re-identification. Initially, an interleaved feature pyramid module (IFPM) was designed, which recursively transmits rich semantic information from high-level feature maps to the bottom layer through interleaved connections, achieving the extraction of multi-scale information. Secondly, a multi-scale feature dynamic fusion module (MDFM) to effectively integrate multi-scale information in IFPM and achieve cross-scale feature fusion. It allows the network to dynamically select the most suitable features for fusion based on pedestrian characteristics and size. Finally, the designed feature interaction module (FIM) uses different semantic part features as graph nodes, allowing information transfer between nodes, suppressing the transfer of meaningless feature information such as occlusion, promoting the transfer of semantic feature information, and effectively alleviating occlusion problems. Extensive experimental results on both occluded and holistic datasets demonstrate the efficacy of our approach.