Person Re‐identification Based on Feature Erasure and Diverse Feature Learning

Abstract

Robust and comprehensive modelling of targets is the key to successful person re-identification. However, some useful information may be ignored, since CNNs tend to learn from the most distinctive feature region of the human body. In the present study, a multi-branch lightweight network structure that can enhance the ability of diverse feature retrieval is introduced. The proposed network consists of three branches. In the feature erasure branch, a drop block model is added to remove the horizontal region with the highest activation degree from feature vectors so as to allow the network to learn relatively low discrimination features. The global branch is used as an essential supplement to the feature erasure branch. A unified horizontal segmentation strategy is adopted in the local branch to avoid the influence of feature dislocation. Finally, diverse feature learning is achieved through the branch network structure. The proposed method can achieve state-of-the-art results on Market-1501, CUHK03 and DukeMTMC-Reid data sets, thereby demonstrating the effectiveness of the method.