  Unsupervised Domain Adaptation (UDA) methods for person Re-Identification
(Re-ID) rely on target domain samples to model the marginal distribution of the
data. To deal with the lack of target domain labels, UDA methods leverage
information from labeled source samples and unlabeled target samples. A
promising approach relies on the use of unsupervised learning as part of the
pipeline, such as clustering methods. The quality of the clusters clearly plays
a major role in methods performance, but this point has been overlooked. In
this work, we propose a multi-step pseudo-label refinement method to select the
best possible clusters and keep improving them so that these clusters become
closer to the class divisions without knowledge of the class labels. Our
refinement method includes a cluster selection strategy and a camera-based
normalization method which reduces the within-domain variations caused by the
use of multiple cameras in person Re-ID. This allows our method to reach
state-of-the-art UDA results on DukeMTMC-Market1501 (source-target). We surpass
state-of-the-art for UDA Re-ID by 3.4% on Market1501-DukeMTMC datasets, which
is a more challenging adaptation setup because the target domain (DukeMTMC) has
eight distinct cameras. Furthermore, the camera-based normalization method
causes a significant reduction in the number of iterations required for
training convergence.

Computer Vision and Pattern Recognition;Artificial Intelligence