Double-Resolution Attention Network for Person Re-Identification

In person re-identification (ReID) task, some information will be lost in the process of extracting identity-related features, causing the basis for identification become to less and then affects the performance of model. This paper proposes a person ReID method based on double-resolution feature and channel attention mechanism. Firstly, a high-resolution feature branch is added on ResNet, and generate feature vectors corresponding to eight different regions by applying pooling layer on different resolution feature maps. Then a channel attention module is designed based on the situation of feature vectors to enhance the expressive ability of the effective part. Finally, batch normalization is used to coordinate classification loss and measurement loss. In the ablation experiment, the application of each step in the algorithm effectively improves the performance of the model. In the comparative experiments on Market-1501, DUKEMTMC-REID, and CUHK03 datasets, the mean average precision and rank-1 of the proposed algorithm are evidently improved than that of other recent representative algorithms. Experimental results demonstrate that the proposed method can improve the accuracy of person ReID by combining more abundant features.