Generalized optimal sub-pattern assignment metric
This provides a more sound and flexible metric for researchers and practitioners in MTT to assess algorithm performance, though it is incremental over the existing OSPA metric.
The paper tackles the problem of evaluating multiple target tracking (MTT) algorithms by introducing the generalized optimal sub-pattern assignment (GOSPA) metric, which penalizes localization errors, missed targets, and false targets in a unified way, and extends it to random finite sets for rigorous simulation-based evaluation.
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.