  To help accelerate progress in multi-target, multi-camera tracking systems,
we present (i) a new pair of precision-recall measures of performance that
treats errors of all types uniformly and emphasizes correct identification over
sources of error; (ii) the largest fully-annotated and calibrated data set to
date with more than 2 million frames of 1080p, 60fps video taken by 8 cameras
observing more than 2,700 identities over 85 minutes; and (iii) a reference
software system as a comparison baseline. We show that (i) our measures
properly account for bottom-line identity match performance in the multi-camera
setting; (ii) our data set poses realistic challenges to current trackers; and
(iii) the performance of our system is comparable to the state of the art.
