  Person re-identification (Re-ID) has been a significant research topic in the
past decade due to its real-world applications and research significance. While
supervised person Re-ID methods achieve superior performance over unsupervised
counterparts, they can not scale to large unlabelled datasets and new domains
due to the prohibitive labelling cost. Therefore, unsupervised person Re-ID has
drawn increasing attention for its potential to address the scalability issue
in person Re-ID. Unsupervised person Re-ID is challenging primarily due to
lacking identity labels to supervise person feature learning. The corresponding
solutions are diverse and complex, with various merits and limitations.
Therefore, comprehensive surveys on this topic are essential to summarise
challenges and solutions to foster future research. Existing person Re-ID
surveys have focused on supervised methods from classifications and
applications but lack detailed discussion on how the person Re-ID solutions
address the underlying challenges. This survey review recent works on
unsupervised person Re-ID from the perspective of challenges and solutions.
Specifically, we provide an in-depth analysis of highly influential methods
considering the four significant challenges in unsupervised person Re-ID: 1)
lacking ground-truth identity labels to supervise person feature learning; 2)
learning discriminative person features with pseudo-supervision; 3) learning
cross-camera invariant person feature, and 4) the domain shift between
datasets. We summarise and analyse evaluation results and provide insights on
the effectiveness of the solutions. Finally, we discuss open issues and suggest
some promising future research directions.
