Integrating Coarse Granularity Part-Level Features with Supervised Global-Level Features for Person Re-Identification

• 来源期刊: 中兴通讯技术（英文版）
• 年，卷（期）: 2021,（1）
• 所属期刊栏目: Research Paper
• 页码范围: 72-81
• 页数: 10页
• 语种: 英文
• DOI: 10.12142/ZTECOM.202101009

摘要

Person re-identification (Re-ID) has achieved great progress in recent years. However, person Re-ID methods are still suffering from body part missing and occlusion problems, which makes the learned representations less reliable. In this paper, we pro?pose a robust coarse granularity part-level network (CGPN) for person Re-ID, which ex?tracts robust regional features and integrates supervised global features for pedestrian im?ages. CGPN gains two-fold benefit toward higher accuracy for person Re-ID. On one hand, CGPN learns to extract effective regional features for pedestrian images. On the other hand, compared with extracting global features directly by backbone network, CGPN learns to extract more accurate global features with a supervision strategy. The single mod?el trained on three Re-ID datasets achieves state-of-the-art performances. Especially on CUHK03, the most challenging Re-ID dataset, we obtain a top result of Rank-1/mean av?erage precision (mAP)=87.1％/83.6％without re-ranking.