论文标题
将粗粒度零件级特征与有监督的人重新识别的全球级别特征集成
Integrating Coarse Granularity Part-level Features with Supervised Global-level Features for Person Re-identification
论文作者
论文摘要
近年来，整体人士重新识别（重新识别）和部分人的重新确认分别取得了长足的进步。但是，实际上，场景通常包括整体和部分行人图像，这使得单一的整体或部分人都难以工作。在本文中，我们提出了一个强大的粗粒度零件级别的人re-id网络（CGPN），该网络不仅提取了强大的区域层面特征，而且还整合了整体和部分人图像的监督全球特征。 CGPN获得了重新ID的更高准确性的两倍受益。一方面，CGPN学会了为整体和部分人图像提取有效的身体部位特征。另一方面，与直接通过Backbone网络提取全局功能相比，CGPN学会通过监督策略提取更准确的全局功能。在包括Market-1501，Dukemtmc-Reid和Cuhk03在内的三个RE-ID数据集上训练的单个模型可以实现最先进的性能，并胜过任何现有方法。尤其是在单个查询模式下的人重新ID数据集的CUHK03上，我们通过此方法获得了rank-1/map = 87.1 \％/83.6 \％的最高结果，而无需重新排列，以+7.0 \％/ +6.7 \％的+7.0.0 \％/ +6.7 \％\％。
Holistic person re-identification (Re-ID) and partial person re-identification have achieved great progress respectively in recent years. However, scenarios in reality often include both holistic and partial pedestrian images, which makes single holistic or partial person Re-ID hard to work. In this paper, we propose a robust coarse granularity part-level person Re-ID network (CGPN), which not only extracts robust regional level body features, but also integrates supervised global features for both holistic and partial person images. CGPN gains two-fold benefit toward higher accuracy for person Re-ID. On one hand, CGPN learns to extract effective body part features for both holistic and partial person 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 including Market-1501, DukeMTMC-reID and CUHK03 achieves state-of-the-art performances and outperforms any existing approaches. Especially on CUHK03, which is the most challenging dataset for person Re-ID, in single query mode, we obtain a top result of Rank-1/mAP=87.1\%/83.6\% with this method without re-ranking, outperforming the current best method by +7.0\%/+6.7\%.
