Generalized optimal sub-pattern assignment metric

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.
Comment: The paper received the Jean Pierre Le Cadre best paper award at the 20th International Conference on Information Fusion, July 2017. A Matlab implementation of the proposed GOSPA metric is available in https://github.com/abusajana/GOSPA Also visit https://youtu.be/M79GTTytvCM for a 15-min presentation about the paper