Multi-Scan Generalized Labeled Multi-Bernoulli Filter
© 2018 ISIF This paper extends the generalized labeled multi-Bernoulli (GLMB) tracking filter to a batch multi-target tracker. In a labeled random finite set formulation, a multi-target tracking filter propagates the labeled multi-target filtering density while a batch multi-target tracker propagate...
| Main Authors: | , |
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| Format: | Conference Paper |
| Published: |
2018
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| Online Access: | http://purl.org/au-research/grants/arc/DP170104854 http://hdl.handle.net/20.500.11937/71807 |
| _version_ | 1848762578038161408 |
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| author | Vo, Ba Tuong Vo, Ba-Ngu |
| author_facet | Vo, Ba Tuong Vo, Ba-Ngu |
| author_sort | Vo, Ba Tuong |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | © 2018 ISIF This paper extends the generalized labeled multi-Bernoulli (GLMB) tracking filter to a batch multi-target tracker. In a labeled random finite set formulation, a multi-target tracking filter propagates the labeled multi-target filtering density while a batch multi-target tracker propagates the labeled multi-target posterior density. The GLMB filter is an analytic solution to the labeled multi-target filtering recursion. In this work, we show that the GLMB filter can be extended to an analytic multi-object posterior recursion. |
| first_indexed | 2025-11-14T10:49:47Z |
| format | Conference Paper |
| id | curtin-20.500.11937-71807 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:49:47Z |
| publishDate | 2018 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-718072022-10-27T07:19:24Z Multi-Scan Generalized Labeled Multi-Bernoulli Filter Vo, Ba Tuong Vo, Ba-Ngu © 2018 ISIF This paper extends the generalized labeled multi-Bernoulli (GLMB) tracking filter to a batch multi-target tracker. In a labeled random finite set formulation, a multi-target tracking filter propagates the labeled multi-target filtering density while a batch multi-target tracker propagates the labeled multi-target posterior density. The GLMB filter is an analytic solution to the labeled multi-target filtering recursion. In this work, we show that the GLMB filter can be extended to an analytic multi-object posterior recursion. 2018 Conference Paper http://hdl.handle.net/20.500.11937/71807 10.23919/ICIF.2018.8455419 http://purl.org/au-research/grants/arc/DP170104854 http://purl.org/au-research/grants/arc/DP160104662 restricted |
| spellingShingle | Vo, Ba Tuong Vo, Ba-Ngu Multi-Scan Generalized Labeled Multi-Bernoulli Filter |
| title | Multi-Scan Generalized Labeled Multi-Bernoulli Filter |
| title_full | Multi-Scan Generalized Labeled Multi-Bernoulli Filter |
| title_fullStr | Multi-Scan Generalized Labeled Multi-Bernoulli Filter |
| title_full_unstemmed | Multi-Scan Generalized Labeled Multi-Bernoulli Filter |
| title_short | Multi-Scan Generalized Labeled Multi-Bernoulli Filter |
| title_sort | multi-scan generalized labeled multi-bernoulli filter |
| url | http://purl.org/au-research/grants/arc/DP170104854 http://purl.org/au-research/grants/arc/DP170104854 http://hdl.handle.net/20.500.11937/71807 |