Performance Evaluation for Large-Scale Multi-Target Tracking Algorithms
© 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...
| Main Authors: | , , |
|---|---|
| Format: | Conference Paper |
| Published: |
2018
|
| Online Access: | http://hdl.handle.net/20.500.11937/73225 |
| _version_ | 1848762958511865856 |
|---|---|
| author | Beard, Michael Vo, Ba Tuong Vo, Ba-Ngu |
| author_facet | Beard, Michael Vo, Ba Tuong Vo, Ba-Ngu |
| author_sort | Beard, Michael |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | © 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. |
| first_indexed | 2025-11-14T10:55:50Z |
| format | Conference Paper |
| id | curtin-20.500.11937-73225 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:55:50Z |
| publishDate | 2018 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-732252018-12-13T09:35:32Z Performance Evaluation for Large-Scale Multi-Target Tracking Algorithms Beard, Michael Vo, Ba Tuong Vo, Ba-Ngu © 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. 2018 Conference Paper http://hdl.handle.net/20.500.11937/73225 10.23919/ICIF.2018.8455700 restricted |
| spellingShingle | Beard, Michael Vo, Ba Tuong Vo, Ba-Ngu Performance Evaluation for Large-Scale Multi-Target Tracking Algorithms |
| title | Performance Evaluation for Large-Scale Multi-Target Tracking Algorithms |
| title_full | Performance Evaluation for Large-Scale Multi-Target Tracking Algorithms |
| title_fullStr | Performance Evaluation for Large-Scale Multi-Target Tracking Algorithms |
| title_full_unstemmed | Performance Evaluation for Large-Scale Multi-Target Tracking Algorithms |
| title_short | Performance Evaluation for Large-Scale Multi-Target Tracking Algorithms |
| title_sort | performance evaluation for large-scale multi-target tracking algorithms |
| url | http://hdl.handle.net/20.500.11937/73225 |