  Data associations in multi-target multi-camera tracking (MTMCT) usually
estimate affinity directly from re-identification (re-ID) feature distances.
However, we argue that it might not be the best choice given the difference in
matching scopes between re-ID and MTMCT problems. Re-ID systems focus on global
matching, which retrieves targets from all cameras and all times. In contrast,
data association in tracking is a local matching problem, since its candidates
only come from neighboring locations and time frames. In this paper, we design
experiments to verify such misfit between global re-ID feature distances and
local matching in tracking, and propose a simple yet effective approach to
adapt affinity estimations to corresponding matching scopes in MTMCT. Instead
of trying to deal with all appearance changes, we tailor the affinity metric to
specialize in ones that might emerge during data associations. To this end, we
introduce a new data sampling scheme with temporal windows originally used for
data associations in tracking. Minimizing the mismatch, the adaptive affinity
module brings significant improvements over global re-ID distance, and produces
competitive performance on CityFlow and DukeMTMC datasets.

Computer Vision and Pattern Recognition