A Metric for Performance Evaluation of Multi-Target Tracking Algorithms
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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 distance between two sets of tracks: the ground truth tracks and the set of estimated tracks. This paper proposes a mathematically rigorous metric for this purpose. The basis of the proposed distance measure is the recently formulated consistent metric for performance evaluation of multi-target filters, referred to as the OSPA metric. Multi-target filters sequentially estimate the number of targets and their position in the state space. The OSPA metric is therefore defined on the space of finite sets of vectors. The distinction between filtering and tracking is that tracking algorithms output tracks and a track represents a labeled temporal sequence of state estimates, associated with the same target. The metric proposed in this paper is therefore defined on the space of finite sets of tracks and incorporates the labeling error. Numerical examples demonstrate that the proposed metric behaves in a manner consistent with our expectations.
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Beard, Michael; Vo, Ba Tuong; Vo, Ba-Ngu (2017)© 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 ...
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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 ...