Receding Horizon Estimation for Multi-Target Tracking via Random Finite Set Approach
© 2018 ISIF This paper proposes a robust multi-target tracking algorithm for uncertainty in dynamic motion modeling. To address this issue, the multi-target tracking problem is formulated under random finite set (RFS) framework with finite length memory filtering called receding horizon estimation (...
| Main Author: | |
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| Format: | Conference Paper |
| Published: |
2018
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| Online Access: | http://hdl.handle.net/20.500.11937/72681 |
| _version_ | 1848762814937694208 |
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| author | Kim, Du Yong |
| author_facet | Kim, Du Yong |
| author_sort | Kim, Du Yong |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | © 2018 ISIF This paper proposes a robust multi-target tracking algorithm for uncertainty in dynamic motion modeling. To address this issue, the multi-target tracking problem is formulated under random finite set (RFS) framework with finite length memory filtering called receding horizon estimation (RHE). The proposed algorithm is based on the generalized labeled multi-Bernoulli (GLMB) filter which enables RHE for multi-target tracking. The proposed algorithm, a Receding Horizon GLMB (RH-GLMB) filter, is evaluated through a numerical example and visual tracking datasets where dynamic modeling uncertainty exists. |
| first_indexed | 2025-11-14T10:53:33Z |
| format | Conference Paper |
| id | curtin-20.500.11937-72681 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:53:33Z |
| publishDate | 2018 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-726812018-12-13T09:32:29Z Receding Horizon Estimation for Multi-Target Tracking via Random Finite Set Approach Kim, Du Yong © 2018 ISIF This paper proposes a robust multi-target tracking algorithm for uncertainty in dynamic motion modeling. To address this issue, the multi-target tracking problem is formulated under random finite set (RFS) framework with finite length memory filtering called receding horizon estimation (RHE). The proposed algorithm is based on the generalized labeled multi-Bernoulli (GLMB) filter which enables RHE for multi-target tracking. The proposed algorithm, a Receding Horizon GLMB (RH-GLMB) filter, is evaluated through a numerical example and visual tracking datasets where dynamic modeling uncertainty exists. 2018 Conference Paper http://hdl.handle.net/20.500.11937/72681 10.23919/ICIF.2018.8455261 restricted |
| spellingShingle | Kim, Du Yong Receding Horizon Estimation for Multi-Target Tracking via Random Finite Set Approach |
| title | Receding Horizon Estimation for Multi-Target Tracking via Random Finite Set Approach |
| title_full | Receding Horizon Estimation for Multi-Target Tracking via Random Finite Set Approach |
| title_fullStr | Receding Horizon Estimation for Multi-Target Tracking via Random Finite Set Approach |
| title_full_unstemmed | Receding Horizon Estimation for Multi-Target Tracking via Random Finite Set Approach |
| title_short | Receding Horizon Estimation for Multi-Target Tracking via Random Finite Set Approach |
| title_sort | receding horizon estimation for multi-target tracking via random finite set approach |
| url | http://hdl.handle.net/20.500.11937/72681 |