Domain adaptation for cross-dataset person re-identification

Abstract

Most of the studies that have been conducted on person re-identification utilizes a single dataset to train, validate, and test the proposed system. Although these subsets do not overlap, since they were collected under similar conditions, experimental results obtained from such a setup are not good indicators in terms of the generalizability of the developed systems. Therefore, to obtain a better measure for the generalization capability of the proposed systems, cross-dataset experimental setups would be more appropriate. In the cross-dataset setup, the developed systems are trained and validated on one dataset and then tested using another one. In this work, to reduce the difference between the distributions of the utilized datasets in a cross-dataset setup, we proposed a cycle-consistent generative adversarial network based deep learning approach. The proposed method makes source dataset and target dataset look more similar. In the experiments, Market-1501 dataset was used as the source and PRID2011 was used as the target dataset. In the experiments, by benefiting from the proposed domain adaptation method, superior results have been achieved.