Asymmetric metric learning approach based on distribution constraints for unsupervised person re-identification

In unsupervised person re-identification, to solve the problem that the traditional asymmetric metric learning method cannot overcome the difference of data distribution from different views, an asymmetric metric learning approach based on distribution constraints for unsupervised person re-identification is proposed. Firstly, JSTL technology is used to pre-train the feature extraction network to obtain a robust feature representation. Then, the asymmetric metric learning method based on distribution constraints is proposed. By introducing distribution constraints into the traditional asymmetric metric learning objective function, this method not only realizes the asymmetric feature transformation of person images from different camera views, but also effectively overcomes the problem of low recognition accuracy caused by the difference of person data distribution. Finally, the objective function is optimized by using the gradient descent method, and the optimal measure matrix is obtained by solving the generalized eigenvalue problem. Experiments are implemented on Market and Duke datasets, and the results show that the rank1 value of the algorithm is 57.01 % and 32.32 %, the MAP value is 27.91 % and 16.00 %, respectively, and the recognition performance of the algorithm is significantly improved compared with the traditional asymmetric metric learning algorithm. © 2023 Northeast University. All rights reserved.