  Person Re-ID has been gaining a lot of attention and nowadays is of
fundamental importance in many camera surveillance applications. The task
consists of identifying individuals across multiple cameras that have no
overlapping views. Most of the approaches require labeled data, which is not
always available, given the huge amount of demanded data and the difficulty of
manually assigning a class for each individual. Recently, studies have shown
that re-ranking methods are capable of achieving significant gains, especially
in the absence of labeled data. Besides that, the fusion of feature extractors
and multiple-source training is another promising research direction not
extensively exploited. We aim to fill this gap through a manifold rank
aggregation approach capable of exploiting the complementarity of different
person Re-ID rankers. In this work, we perform a completely unsupervised
selection and fusion of diverse ranked lists obtained from multiple and diverse
feature extractors. Among the contributions, this work proposes a query
performance prediction measure that models the relationship among images
considering a hypergraph structure and does not require the use of any labeled
data. Expressive gains were obtained in four datasets commonly used for person
Re-ID. We achieved results competitive to the state-of-the-art in most of the
scenarios.
