  Designing real-world person re-identification (re-id) systems requires
attention to operational aspects not typically considered in academic research.
Typically, the probe image or image sequence is matched to a gallery set with a
fixed candidate list. On the other hand, in real-world applications of re-id,
we would search for a person of interest in a gallery set that is continuously
populated by new candidates over time. A key question of interest for the
operator of such a system is: how long is a correct match to a probe likely to
remain in a rank-k shortlist of candidates? In this paper, we propose to
distill this information into what we call a Rank Persistence Curve (RPC),
which unlike a conventional cumulative match characteristic (CMC) curve helps
directly compare the temporal performance of different re-id algorithms. To
carefully illustrate the concept, we collected a new multi-shot person re-id
dataset called RPIfield. The RPIfield dataset is constructed using a network of
12 cameras with 112 explicitly time-stamped actor paths among about 4000
distractors. We then evaluate the temporal performance of different re-id
algorithms using the proposed RPCs using single and pairwise camera videos from
RPIfield, and discuss considerations for future research.
