Person Re-Identification via Group Symmetry Theory

Abstract

In recent years, deep learning represented by convolutional neural networks (CNNs) has developed rapidly. The development of deep learning has also led to rapid progress in the field of person re-identification. Many related researchers have begun to use deep learning to solve the problem of person re-identification. The existing deep learning methods for person re-identification mainly use the convolutional neural networks to extract features. The middle layers of the convolutional neural networks contain a wealth of structural information but the previous methods have not fully exploited them. This paper proposes a network named ResGroupNet that uses group symmetry theory to constrain the middle structure of ResNet-50. In detail, we added a branch at the fourth layer of the backbone, the branch was implemented based on theory, at each tail to the backbone and branch, we use sphere loss and triplet loss, respectively. According to our survey, we are the first to introduce the group theory into the ReID task. The experiments show that the proposed method is effective and have achieved good results on the Market-1501, DukeMTMC-reID, and CUHK03-NP datasets.

Keywords

• Person re-identification
• convolutional neural networks
• symmetric group
• deep learning
• metric learning