Integrating Coarse Granularity Part-Level Features with Supervised Global-Level Features for Person
CAO Jiahao1,2, MAO Xiaofei1,2, LI Dongfang1,2, ZHENG Qingfang1,2, JIA Xia1,2
(1. State Key Laboratory of Mobile Network and Mobile Multimedia Technology, Shenzhen 518057, China;
2. ZTE Corporation, Shenzhen 518057, China)
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 propose a robust coarse granularity part-level network (CGPN) for person Re-ID, which extracts robust regional features and integrates supervised global features for pedestrian images. 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 model 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 average precision (mAP)=87.1%/83.6% without re-ranking.
person Re-ID; supervision; coarse granularity