  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\%.
