Person re-identification (re-ID) in the scenario with large spatial and temporal spans has not been fully explored. This is partially because that, existing benchmark datasets were mainly collected with limited spatial and temporal ranges, e.g., using videos recorded in a few days by cameras in a specific region of the campus. Such limited spatial and temporal ranges make it hard to simulate the difficulties of person re-ID in real scenarios. In this work, we contribute a novel Large-scale Spatio-Temporal (LaST) person re-ID dataset, including 10,860 identities with more than 224k images. Compared with existing datasets, LaST presents more challenging and high-diversity re-ID settings, and significantly larger spatial and temporal ranges. For instance, each person can appear in different cities or countries, and in various time slots from daytime to night, and in different seasons from spring to winter. To our best knowledge, LaST is a novel person re-ID dataset with the largest spatiotemporal ranges. Based on LaST, we verified its challenge by conducting a comprehensive performance evaluation of 14 re-ID algorithms. We further propose an easy-to-implement baseline that works well on such challenging re-ID setting. We also verified that models pre-trained on LaST can generalize well on existing datasets with short-term and cloth-changing scenarios. We expect LaST to inspire future works toward more realistic and challenging re-ID tasks. More information about the dataset is available at https://github.com/shuxjweb/last.git.

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Unsupervised video person re‐identification (reID) methods usually depend on global‐level features. Many supervised reID methods employed local‐level features and achieved significant performance improvements. However, applying local‐level features to unsupervised methods may introduce an unstable performance. To improve the performance stability for unsupervised video reID, this paper introduces a general scheme fusing part models and unsupervised learning. In this scheme, the global‐level feature is divided into equal local‐level feature. A local‐aware module is employed to explore the potentials of local‐level feature for unsupervised learning. A global‐aware module is proposed to overcome the disadvantages of local‐level features. Features from these two modules are fused to form a robust feature representation for each input image. This feature representation has the advantages of local‐level feature without suffering from its disadvantages. Comprehensive experiments are conducted on three benchmarks, including PRID2011, iLIDS‐VID, and DukeMTMC‐VideoReID, and the results demonstrate that the proposed approach achieves state‐of‐the‐art performance. Extensive ablation studies demonstrate the effectiveness and robustness of proposed scheme, local‐aware module and global‐aware module. The code and generated features are available at https://github.com/deropty/uPMnet.

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