Method and system for person re-identification

Abstract

The present application discloses a method and a system for person re-identification, the method including: inputting a training set to a model-to-be-trained, and determining a single-class label and memory features of each image data in the training set; determining multi-class labels through positive label prediction according to the single-class labels and a memory feature set; determining classification scores according to image features of each image data in the training set and the memory feature set; determining a multi-label classification loss according to the multi-class labels and the classification scores; and updating and training the model-to-be-trained to obtain a re-identification model according to the multi-label classification loss. The classification scores are determined according to the image features of each image data in the training set and the memory feature set, which is not affected by the domain gap; the multi-class labels are determined through positive label prediction according to the single-class labels and the memory feature set; then, the multi-label classification loss is determined according to the multi-class labels and the classification scores, and the model-to-be-trained is updated and trained, so that the resulting re-identification model has high performance, strong robustness and low cost.