Exploiting Robust Unsupervised Video Person Re-identification

TL;DR

This paper proposes a novel unsupervised video person re-identification method that fuses global and local features using dedicated modules, resulting in improved stability and state-of-the-art performance across multiple benchmarks.

Contribution

It introduces a general scheme combining part models with unsupervised learning, employing local-aware and global-aware modules to enhance feature robustness.

Findings

Achieves state-of-the-art results on PRID2011, iLIDS-VID, and DukeMTMC-VideoReID datasets.

Demonstrates the effectiveness of fusing local and global features for unsupervised reID.

Shows robustness and stability improvements through extensive ablation studies.

Abstract

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.