Performance Evaluation for Large-Scale Multi-Target Tracking Algorithms
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© 2018 ISIF The traditional method of applying the optimal subpattern assignment (OSPA) metric cannot fully evaluate multitarget tracking performance, as it does not account for phenomena such as track label switching, and track fragmentation. The OSPA(2)has been proposed as a technique for applying the OSPA distance in a way that captures these effects, while retaining the properties of a true metric. In this paper, we demonstrate the behaviour of the OSPA(2)on some numerical examples, discuss some of its advantages and limitations, and show that it is capable of being applied to performance evaluation of large-scale scenarios in the order of a thousand targets.
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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 ...
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 ...
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 ...