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dc.contributor.authorBeard, Michael
dc.contributor.authorVo, Ba Tuong
dc.contributor.authorVo, Ba-Ngu
dc.date.accessioned2018-12-13T09:15:49Z
dc.date.available2018-12-13T09:15:49Z
dc.date.created2018-12-12T02:47:05Z
dc.date.issued2018
dc.identifier.citationBeard, M. and Vo, B.T. and Vo, B. 2018. Performance Evaluation for Large-Scale Multi-Target Tracking Algorithms, pp. 1575-1581.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/73225
dc.identifier.doi10.23919/ICIF.2018.8455700
dc.description.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.

dc.titlePerformance Evaluation for Large-Scale Multi-Target Tracking Algorithms
dc.typeConference Paper
dcterms.source.startPage1575
dcterms.source.endPage1581
dcterms.source.title2018 21st International Conference on Information Fusion, FUSION 2018
dcterms.source.series2018 21st International Conference on Information Fusion, FUSION 2018
dcterms.source.isbn9780996452762
curtin.departmentSchool of Electrical Engineering, Computing and Mathematical Science (EECMS)
curtin.accessStatusFulltext not available


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