LAUSR

Person re-identification (ReID) identifies object IDs in a multicamera environment based on local tracking of city surveillance cameras in public places. This method can improve the performance by learning various features using a convolutional neural network. However, ReID methods are limited in terms of application in practical surveillance environments as the ReID model is trained on public datasets and lacks the ability to generalize images acquired using other cameras. Moreover, although various methods to improve the ReID performance have been proposed, most existing studies did not evaluate ReID performances according to the number of parameters, i.e., ReID models that can be used in a limited memory environment were not considered. In this study, we propose a Tiny Asymmetric Feature Normalized Network, which can be generalized to test atasets acquired in real surveillance environments considering various scale features and can be operated with a limited number of parameters. Moreover, the Gwangju Institute of Science and Technology Practical Person ReID (GPP-reID) dataset, which was used to evaluate the performance of the ReID model, has been distributed and made available to enable applications in real-world surveillance environments. Our proposed method achieved mean average precision (mAP), Rank-1 values of 86.2, 94.7 and 74.8, 85.9 on the Market1501 and Duke Multitracking Multicamera ReID datasets, respectively. In addition, mAP and Rank-1 values of 44.2 and 64.1, respectively, were achieved on the cross-validated, new benchmark dataset, GPP-reID, using a network with one-tenth the number of parameters as the 50-layer residual network.