  Multi-target multi-camera tracking (MTMCT) systems track targets across
cameras. Due to the continuity of target trajectories, tracking systems usually
restrict their data association within a local neighborhood. In single camera
tracking, local neighborhood refers to consecutive frames; in multi-camera
tracking, it refers to neighboring cameras that the target may appear
successively. For similarity estimation, tracking systems often adopt
appearance features learned from the re-identification (re-ID) perspective.
Different from tracking, re-ID usually does not have access to the trajectory
cues that can limit the search space to a local neighborhood. Due to its global
matching property, the re-ID perspective requires to learn global appearance
features. We argue that the mismatch between the local matching procedure in
tracking and the global nature of re-ID appearance features may compromise
MTMCT performance.
  To fit the local matching procedure in MTMCT, in this work, we introduce
locality aware appearance metric (LAAM). Specifically, we design an
intra-camera metric for single camera tracking, and an inter-camera metric for
multi-camera tracking. Both metrics are trained with data pairs sampled from
their corresponding local neighborhoods, as opposed to global sampling in the
re-ID perspective. We show that the locally learned metrics can be successfully
applied on top of several globally learned re-ID features. With the proposed
method, we report new state-of-the-art performance on the DukeMTMC dataset, and
a substantial improvement on the CityFlow dataset.
