OSPA<sup>(2)</sup>: Using the OSPA metric to evaluate multi-target tracking performance
MetadataShow full item record
© 2017 IEEE. The optimal sub-pattern assignment (OSPA) metric is a distance between two sets of points that jointly accounts for the dissimilarity in the number of points and the values of the points in the respective sets. The OSPA metric is often used for measuring the distance between two sets of points in Euclidean space. A common example is in multi-target filtering, where the aim is to estimate the set of current target states, all of which have the same dimension. In multi-target tracking (MTT), the aim is to estimate the set of target tracks over a period of time, rather than the set of target states at each time step. In this case, it is not sufficient to analyse the multi-target filtering error at each time step in isolation. It is important that a metric for evaluating MTT performance accounts for the dissimilarity between the overall target tracks, which are generally of different dimensions. In this paper, we demonstrate that MTT error can be captured using the OSPA metric to define a distance between two sets of tracks.
Showing items related by title, author, creator and subject.
Ristic, B.; Vo, Ba-Ngu; Clark, D.; Vo, Ba Tuong (2011)Performance evaluation of multi-target tracking algorithms is of great practical importance in the design, parameter optimization and comparison of tracking systems. The goal of performance evaluation is to measure the ...
Beard, Michael; Arulampalam, S. (2012)The performance of three multi-target tracking algorithms are compared under the challenging problem of bearings-only tracking in the presence of clutter and missed detections. The algorithms under consideration are the ...
Nagappa, S.; Clark, D.; Mahler, Ronald (2011)This paper proposes the use of the Hellinger distance in evaluating the localisation error in the OSPA metric. The Hellinger distance provides a measure of the difference between two distributions and is used here to ...