Generalized optimal sub-pattern assignment metric
For researchers in multi-target tracking, this provides a more principled and interpretable performance metric that aligns with traditional evaluation measures.
The paper introduces the GOSPA metric for evaluating multi-target tracking algorithms, which improves upon the OSPA metric by unnormalizing cardinality and enabling assignment-based optimization, allowing separate penalization of localization, missed, and false target errors.
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.