A Frequency Reinforce and Information Mutual Embedding Network for Occluded Person Re-Identification

Occluded person re-identification (ReID) is vital for enhancing immersion and realism in virtual reality (VR) applications, particularly in fields such as security, surveillance, and interactive environments. Existing methods primarily concentrate on predicting occlusions or enhancing features, but they often neglect the issue of data imbalance, which restricts their effectiveness in complex VR scenarios. To address these challenges, we propose a novel network, the Frequency Reinforcement and Information Mutual Embedding Network (FRIME-Net), specifically designed to enhance the accuracy of occluded person retrieval in VR environments. FRIME-Net is a dual-branch network that combines ViT and CNN to capture a diverse range of person features. FRIME-Net features two innovative components: a high-low frequency reinforcement module that captures both coarse-grained and fine-grained features, and a full-part mutual embedding module that integrates global and part-based information, thereby improving feature robustness. Experimental results on three widely-used datasets, including both occluded and non-occluded settings, demonstrate the effectiveness of FRIME-Net, underscoring its potential to improve VR applications that require robust person re-identification capabilities.