Progressive spatial-temporal transfer model for unsupervised person re-identification

Over the past decade, a more widespread area of computer vision research has been person re-identification (P-Reid). This technology is applied in fields such as pedestrian tracking, security, and video surveillance. Currently, person re-identification performs well when supervised with labeled data, but accuracy frequently suffers when learning unsupervised on unlabeled samples. Therefore, improving unlabeled samples model is a challenging endeavor. In order to solve this problem, we propose a progressive spatial-temporal transfer model (PSTT), which consists of three stages, including incremental tuning, spatial-temporal fusion and target domain learning. In the first stage, a high-performance multi-scale network that can initially cluster samples is obtained through triplet loss function. In the next stage, to mine spatial-temporal and visual semantic information, we introduce a fusion model that fuses the visual information extracted from the labeled dataset and the unlabeled dataset using a trained network with its spatial-temporal information. In the final stage, with the assistance of fusion model, we employ a strategy that extends learning from labeled to unlabeled samples. During the training, the fusion model is used to select labeled and unlabeled samples, and multiple meta loss function is used for transfer learning. During the testing, the fusion model is employed to enhance the accuracy of network. In the experiment, we evaluate our method on five standard P-Reid benchmarks: Market1501, DukeMTMC-ReID, CUHK03, MSMT17 and Occluded-DukeMTMC. Extensive experiments show that our proposed PSTT achieves state-of-the-art performance, exceeding the previous method by a certain margin. The source code is available at https://github.com/LiZX12/PSTT.