Robust Multi-target Tracking with Bootstrapped-GLMB Filter
This dissertation presents novel multi-target tracking algorithms that obviate the need for prior knowledge of system parameters such as clutter rate, detection probabilities, and birth models. Information on these parameters is unknown but important to tracking performance. The proposed algorithms...
| Main Author: | |
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| Format: | Thesis |
| Published: |
Curtin University
2022
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| Online Access: | http://hdl.handle.net/20.500.11937/88811 |
| _version_ | 1848765089700642816 |
|---|---|
| author | Do, Cong-Thanh |
| author_facet | Do, Cong-Thanh |
| author_sort | Do, Cong-Thanh |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | This dissertation presents novel multi-target tracking algorithms that obviate the need for prior knowledge of system parameters such as clutter rate, detection probabilities, and birth models. Information on these parameters is unknown but important to tracking performance. The proposed algorithms exploit the advantages of existing RFS trackers and filters by bootstrapping them. This configuration inherits the efficiency of tracking target trajectories from the RFS trackers and low complexity in parameter estimation from the RFS filters. |
| first_indexed | 2025-11-14T11:29:43Z |
| format | Thesis |
| id | curtin-20.500.11937-88811 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:29:43Z |
| publishDate | 2022 |
| publisher | Curtin University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-888112022-06-30T05:50:15Z Robust Multi-target Tracking with Bootstrapped-GLMB Filter Do, Cong-Thanh This dissertation presents novel multi-target tracking algorithms that obviate the need for prior knowledge of system parameters such as clutter rate, detection probabilities, and birth models. Information on these parameters is unknown but important to tracking performance. The proposed algorithms exploit the advantages of existing RFS trackers and filters by bootstrapping them. This configuration inherits the efficiency of tracking target trajectories from the RFS trackers and low complexity in parameter estimation from the RFS filters. 2022 Thesis http://hdl.handle.net/20.500.11937/88811 Curtin University fulltext |
| spellingShingle | Do, Cong-Thanh Robust Multi-target Tracking with Bootstrapped-GLMB Filter |
| title | Robust Multi-target Tracking
with Bootstrapped-GLMB Filter |
| title_full | Robust Multi-target Tracking
with Bootstrapped-GLMB Filter |
| title_fullStr | Robust Multi-target Tracking
with Bootstrapped-GLMB Filter |
| title_full_unstemmed | Robust Multi-target Tracking
with Bootstrapped-GLMB Filter |
| title_short | Robust Multi-target Tracking
with Bootstrapped-GLMB Filter |
| title_sort | robust multi-target tracking
with bootstrapped-glmb filter |
| url | http://hdl.handle.net/20.500.11937/88811 |