Abstract: This paper proposes a new person re-identification (ReID) method based on multi-branch feature fusion, in order to solve the problem that current person ReID cannot make full use of effective feature information for identification. Firstly, each of the last 3 convolution blocks is connected to a respective branch. Secondly, approaches such as attentional mechanism and batch feature erasing (BFE) are used to deal with the feature of each branch. Finally, the feature of each branch is fused to obtain the high fine-grained representational feature. The 3 branches monitor each other during training. Single-domain and cross-domain experiments have been conducted to evaluate the performance of our proposed method on Market1501、DukeMTMC-reID、CUHK03 and MSMT17 benchmark datasets. Results show that the proposed method outperforms other state-of-the-art techniques. Rank-1 and mAP on CUHK03 are 76.6% and 72.8%, respectively.

Key words: person re-identification, multi-branch feature, feature fusion, cross-domain, mutual monitoring