A generalized labeled multi-bernoulli filter with object spawning
Previous labeled random finite set filter developments use a motion model that only accounts for survival and birth. While such a model provides the means for a multi-object tracking filter, such as the generalized labeled multi-Bernoulli (GLMB) filter to capture object births and deaths in a wide v...
| Main Authors: | , , , |
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| Format: | Journal Article |
| Published: |
IEEE
2018
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| Online Access: | http://purl.org/au-research/grants/arc/DP170104854 http://hdl.handle.net/20.500.11937/72442 |
| _version_ | 1848762751577489408 |
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| author | Bryant, D. Vo, Ba Tuong Vo, Ba-Ngu Jones, B. |
| author_facet | Bryant, D. Vo, Ba Tuong Vo, Ba-Ngu Jones, B. |
| author_sort | Bryant, D. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Previous labeled random finite set filter developments use a motion model that only accounts for survival and birth. While such a model provides the means for a multi-object tracking filter, such as the generalized labeled multi-Bernoulli (GLMB) filter to capture object births and deaths in a wide variety of applications, it lacks the capability to capture spawned tracks and their lineages. In this paper, we propose a new Generalized Labeled Multi-Bernoulli (GLMB)-based filter that formally incorporates spawning, in addition to birth. This formulation enables the joint estimation of a spawned object's state and information regarding its lineage. Simulations results demonstrate the efficacy of the proposed formulation. |
| first_indexed | 2025-11-14T10:52:33Z |
| format | Journal Article |
| id | curtin-20.500.11937-72442 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:52:33Z |
| publishDate | 2018 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-724422022-10-27T07:19:51Z A generalized labeled multi-bernoulli filter with object spawning Bryant, D. Vo, Ba Tuong Vo, Ba-Ngu Jones, B. Previous labeled random finite set filter developments use a motion model that only accounts for survival and birth. While such a model provides the means for a multi-object tracking filter, such as the generalized labeled multi-Bernoulli (GLMB) filter to capture object births and deaths in a wide variety of applications, it lacks the capability to capture spawned tracks and their lineages. In this paper, we propose a new Generalized Labeled Multi-Bernoulli (GLMB)-based filter that formally incorporates spawning, in addition to birth. This formulation enables the joint estimation of a spawned object's state and information regarding its lineage. Simulations results demonstrate the efficacy of the proposed formulation. 2018 Journal Article http://hdl.handle.net/20.500.11937/72442 10.1109/TSP.2018.2872856 http://purl.org/au-research/grants/arc/DP170104854 http://purl.org/au-research/grants/arc/DP160104662 IEEE restricted |
| spellingShingle | Bryant, D. Vo, Ba Tuong Vo, Ba-Ngu Jones, B. A generalized labeled multi-bernoulli filter with object spawning |
| title | A generalized labeled multi-bernoulli filter with object spawning |
| title_full | A generalized labeled multi-bernoulli filter with object spawning |
| title_fullStr | A generalized labeled multi-bernoulli filter with object spawning |
| title_full_unstemmed | A generalized labeled multi-bernoulli filter with object spawning |
| title_short | A generalized labeled multi-bernoulli filter with object spawning |
| title_sort | generalized labeled multi-bernoulli filter with object spawning |
| url | http://purl.org/au-research/grants/arc/DP170104854 http://purl.org/au-research/grants/arc/DP170104854 http://hdl.handle.net/20.500.11937/72442 |