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.

The combination of global and partial features has been an effective method to improve the precision for Person Re-identification. However, illumination, camera angle and pedestrian pose, etc. still have adverse effects on the retrieval results. In particular, a lot of background and other redundant information is contained in the boundingbox. Meanwhile, the part-based solutions are imprecise on account of unbalanced partitioning. In order to minimize the impact of these factors on the retrieval results, we introduced the pixel, channel attention modules and middle layer supervision into the ReID system to aggregate person features. In this paper, we propose a novel architecture for Person Re-Identification, with the pixel and channel attention modules that are beneficial for feature extraction. Comprehensive experiments results on the mainstream datasets including Market-1501, DukeMTMC-ReId, CUHK03-labeled and CUHK03-detected show that our method achieves better results.