Integrating Coarse Granularity Part-level Features with Supervised Global-level Features for Person Re-identification

• URL: http://arxiv.org/abs/2010.07675v1
• Date: Thu, 15 Oct 2020 11:49:20 GMT
• Title: Integrating Coarse Granularity Part-level Features with Supervised Global-level Features for Person Re-identification
• Authors: Xiaofei Mao, Jiahao Cao, Dongfang Li, Xia Jia, Qingfang Zheng
• Abstract summary: Part-level person Re-ID network (CGPN) integrates supervised global features for both holistic and partial person images. CGPN learns to extract effective body part features for both holistic and partial person images. Single model trained on three Re-ID datasets including Market-1501, DukeMTMC-reID and CUHK03 state-of-the-art performances.
• Score: 3.4758712821739426
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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\%.

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