Person re-identification by multi-division attention

Person re-identification is significant but a challenging task in the computer visual retrieval, which has a wide range of application prospects. Background clutters, arbitrary human pose, and uncontrollable camera angle will greatly hinder person re-identification research. In order to extract more discerning person features, a network architecture based on multi-division attention is proposed in this paper. The network can learn the robust and dis-criminative person feature representation from the global image and different local images simultaneously, which can effectively improve the recognition of person re-identification tasks. In addition, a novel dual local attention network is designed in the local branch, which is composed of spatial attention and channel attention and can optimize the extraction of local features. Experimental results show that the mean average precision of the network on the Market-1501, DukeMTMC-reID, and CUHK03 datasets reaches 82.94%, 72.17%, and 71.76%, respectively.