  Following the popularity of Unsupervised Domain Adaptation (UDA) in person
re-identification, the recently proposed setting of Online Unsupervised Domain
Adaptation (OUDA) attempts to bridge the gap towards practical applications by
introducing a consideration of streaming data. However, this still falls short
of truly representing real-world applications. This paper defines the setting
of Real-world Real-time Online Unsupervised Domain Adaptation (R$^2$OUDA) for
Person Re-identification. The R$^2$OUDA setting sets the stage for true
real-world real-time OUDA, bringing to light four major limitations found in
real-world applications that are often neglected in current research: system
generated person images, subset distribution selection, time-based data stream
segmentation, and a segment-based time constraint. To address all aspects of
this new R$^2$OUDA setting, this paper further proposes Real-World Real-Time
Online Streaming Mutual Mean-Teaching (R$^2$MMT), a novel multi-camera system
for real-world person re-identification. Taking a popular person
re-identification dataset, R$^2$MMT was used to construct over 100 data subsets
and train more than 3000 models, exploring the breadth of the R$^2$OUDA setting
to understand the training time and accuracy trade-offs and limitations for
real-world applications. R$^2$MMT, a real-world system able to respect the
strict constraints of the proposed R$^2$OUDA setting, achieves accuracies
within 0.1% of comparable OUDA methods that cannot be applied directly to
real-world applications.
