Generalized optimal sub-pattern assignment metric

TL;DR

This paper introduces the GOSPA metric, a new way to evaluate multiple target tracking that improves upon existing metrics by better handling cardinality errors and localization, and extends to random finite sets.

Contribution

The paper proposes GOSPA, a generalized metric that unifies and extends existing set-based metrics for multi-target tracking evaluation.

Findings

GOSPA penalizes cardinality errors differently from OSPA.

GOSPA can be expressed as an optimization over assignments.

GOSPA extends to evaluate random finite set-based algorithms.

Abstract

This paper presents the generalized optimal sub-pattern assignment (GOSPA) metric on the space of finite sets of targets. Compared to the well-established optimal sub-pattern assignment (OSPA) metric, GOSPA is unnormalized as a function of the cardinality and it penalizes cardinality errors differently, which enables us to express it as an optimisation over assignments instead of permutations. An important consequence of this is that GOSPA allows us to penalize localization errors for detected targets and the errors due to missed and false targets, as indicated by traditional multiple target tracking (MTT) performance measures, in a sound manner. In addition, we extend the GOSPA metric to the space of random finite sets, which is important to evaluate MTT algorithms via simulations in a rigorous way.