  Person Re-Identification (ReID) across non-overlapping cameras is a
challenging task and, for this reason, most works in the prior art rely on
supervised feature learning from a labeled dataset to match the same person in
different views. However, it demands the time-consuming task of labeling the
acquired data, prohibiting its fast deployment, specially in forensic
scenarios. Unsupervised Domain Adaptation (UDA) emerges as a promising
alternative, as it performs feature-learning adaptation from a model trained on
a source to a target domain without identity-label annotation. However, most
UDA-based algorithms rely upon a complex loss function with several
hyper-parameters, which hinders the generalization to different scenarios.
Moreover, as UDA depends on the translation between domains, it is important to
select the most reliable data from the unseen domain, thus avoiding error
propagation caused by noisy examples on the target data -- an often overlooked
problem. In this sense, we propose a novel UDA-based ReID method that optimizes
a simple loss function with only one hyper-parameter and that takes advantage
of triplets of samples created by a new offline strategy based on the diversity
of cameras within a cluster. This new strategy adapts the model and also
regularizes it, avoiding overfitting on the target domain. We also introduce a
new self-ensembling strategy, in which weights from different iterations are
aggregated to create a final model combining knowledge from distinct moments of
the adaptation. For evaluation, we consider three well-known deep learning
architectures and combine them for final decision-making. The proposed method
does not use person re-ranking nor any label on the target domain, and
outperforms the state of the art, with a much simpler setup, on the Market to
Duke, the challenging Market1501 to MSMT17, and Duke to MSMT17 adaptation
scenarios.
