Person Re-Identification Based on Feature Fusion in AI System

Abstract

In recent years, jointly utilizing local and global features to improve model performance is becoming an important approach for person re-identification. If the relationship between body parts is not considered, it is easy to confuse the identity differentiation of different persons with similar attributes in the corresponding parts. To solve this problem, we propose a feature fusion-based method for person re-identification, which contains three core parts: an adjacency module, a counterfactual attention module and a global difference pooling module. First, an adjacency module is designed to consider the relationship between adjacent body parts and make the features more discriminative. Next, a counterfactual attention module is proposed to conduct counterfactual intervention analysis and encourage the network to learn more useful attention to obtain more fine-grained features. Then, a global difference pooling module is used to learn the global features of a person’s image itself and pay more attention to the important features of the human body. Through the fusion of local features and global features, our model can effectively distinguish the identities of different people with similar attributes in the corresponding parts. Finally, we conduct a large number of experiments and achieve outstanding results on Market-1501, CUHK03 and Msmt17.