Performance Evaluation for Large-Scale Multi-Target Tracking Algorithms
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Authors
Beard, Michael
Vo, Ba Tuong
Vo, Ba-Ngu
Date
2018Type
Conference Paper
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Beard, M. and Vo, B.T. and Vo, B. 2018. Performance Evaluation for Large-Scale Multi-Target Tracking Algorithms, pp. 1575-1581.
Source Title
2018 21st International Conference on Information Fusion, FUSION 2018
ISBN
School
School of Electrical Engineering, Computing and Mathematical Science (EECMS)
Collection
Abstract
© 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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