Unsupervised domain-adaptive person re-identification with multi-camera constraints

TL;DR

This paper introduces an unsupervised domain-adaptive person re-identification method that leverages multi-camera constraints and environment information to improve performance across different domains.

Contribution

It proposes a novel environment-constrained adaptive network that refines pseudo-labels using multi-camera constraints, a first in domain-adaptive learning with such real-world environment data.

Findings

Outperforms state-of-the-art methods on public and private datasets.

Effectively utilizes multi-camera constraints for domain adaptation.

Demonstrates robustness in real environment scenarios.

Abstract

Person re-identification is a key technology for analyzing video-based human behavior; however, its application is still challenging in practical situations due to the performance degradation for domains different from those in the training data. Here, we propose an environment-constrained adaptive network for reducing the domain gap. This network refines pseudo-labels estimated via a self-training scheme by imposing multi-camera constraints. The proposed method incorporates person-pair information without person identity labels obtained from the environment into the model training. In addition, we develop a method that appropriately selects a person from the pair that contributes to the performance improvement. We evaluate the performance of the network using public and private datasets and confirm the performance surpasses state-of-the-art methods in domains with overlapping camera views. To the best of our knowledge, this is the first study on domain-adaptive learning with multi-camera constraints that can be obtained in real environments.