Local-Global Extraction Unit for Person Re-identification

摘要

The huge variance of human pose and inaccurate detection significantly increase the difficulty of person re-identification. Existing deep learning methods mostly focus on extraction of global feature and local feature, or combine them to learn a discriminative pedestrian descriptor. However, rare traditional methods have been exploited the association of the local and global features in convolutional neural networks (CNNs), and some important part-wise information is not captured sufficiently when training. In this paper, we propose a novel architecture called Local-Global Extraction Unit (LGEU), which is able to adaptively re-calibrate part-wise information with integrating the channel-wise information. Extensive experiments on Market-1501, CUHK03, and DukeMTMC-reID datasets achieve competitive results with the state-of-the-art methods. On Market-1501, for instance, LGEU achieves 91.8% rank-1 accuracy and especially 88.0% mAP.