  Unsupervised video person re-identification (reID) methods usually depend on
global-level features. And 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 poentials
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.
