  With the major adoption of automation for cities security, person
re-identification (Re-ID) has been extensively studied recently. In this paper,
we argue that the current way of studying person re-identification, i.e. by
trying to re-identify a person within already detected and pre-cropped images
of people, is not sufficient to implement practical security applications,
where the inputs to the system are the full frames of the video streams. To
support this claim, we introduce the Full Frame Person Re-ID setting (FF-PRID)
and define specific metrics to evaluate FF-PRID implementations. To improve
robustness, we also formalize the hybrid human-machine collaboration framework,
which is inherent to any Re-ID security applications. To demonstrate the
importance of considering the FF-PRID setting, we build an experiment showing
that combining a good people detection network with a good Re-ID model does not
necessarily produce good results for the final application. This underlines a
failure of the current formulation in assessing the quality of a Re-ID model
and justifies the use of different metrics. We hope that this work will
motivate the research community to consider the full problem in order to
develop algorithms that are better suited to real-world scenarios.
