  This paper introduces temporally local metrics for Multi-Object Tracking.
These metrics are obtained by restricting existing metrics based on track
matching to a finite temporal horizon, and provide new insight into the ability
of trackers to maintain identity over time. Moreover, the horizon parameter
offers a novel, meaningful mechanism by which to define the relative importance
of detection and association, a common dilemma in applications where imperfect
association is tolerable. It is shown that the historical Average Tracking
Accuracy (ATA) metric exhibits superior sensitivity to association, enabling
its proposed local variant, ALTA, to capture a wide range of characteristics.
In particular, ALTA is better equipped to identify advances in association
independent of detection. The paper further presents an error decomposition for
ATA that reveals the impact of four distinct error types and is equally
applicable to ALTA. The diagnostic capabilities of ALTA are demonstrated on the
MOT 2017 and Waymo Open Dataset benchmarks.
