Exploiting robust unsupervised video person re‐identification
Xianghao Zang, Ge Li, Wei Gao, Xiujun Shu · IET Image Processing · 2021
Abstract 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 .