  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.
