Person Re-Identification Based on Graph Relation Learning

Person re-identification (Re-ID) aims to find the person across non-overlapping camera views in public places. Existing methods are getting better and better at learning finer pedestrian details, but at the same time they all ignore the relations between these details, which makes some details that are critical to discrimination unable to play a key role in decision-making. To solve this problem, we propose a graph relation learning method for person re-identification. Firstly, we use graph structure to build the relation graph, and then use the weight operation to get the relation vertices that can receive suggestions from other details. Finally, by using the collaborative training scheme to train relation vertices and regional global average features, our model can learn the relation information. Extensive experiments show that the proposed method can effectively improve the discriminative ability of the model, enhance the role of neglected important clues in decision-making, and achieve state-of-the-arts performance on the more challenging CUHK03-NP dataset.