Label Space Partition Selection for Multi-Object Tracking Using Two-Layer Partitioning
Estimating the trajectories of multi-objects poses a significant challenge due to data association ambiguity, which leads to a substantial increase in computational requirements. To address such problems, a divide-and-conquer manner has been employed with parallel computation. In this strategy, dist...
| Main Authors: | , , , , , |
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| Format: | Conference Paper |
| Published: |
2023
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| Online Access: | http://purl.org/au-research/grants/arc/LP200301507 http://hdl.handle.net/20.500.11937/96497 |
| _version_ | 1848766158541422592 |
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| author | Lee, Ji Youn Shim, Changbeom Nguyen, Hoa Van Nguyen, Tran Thien Dat Choi, H. Kim, Y. |
| author_facet | Lee, Ji Youn Shim, Changbeom Nguyen, Hoa Van Nguyen, Tran Thien Dat Choi, H. Kim, Y. |
| author_sort | Lee, Ji Youn |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Estimating the trajectories of multi-objects poses a significant challenge due to data association ambiguity, which leads to a substantial increase in computational requirements. To address such problems, a divide-and-conquer manner has been employed with parallel computation. In this strategy, distinguished objects that have unique labels are grouped based on their statistical dependencies, the intersection of predicted measurements. Several geometry approaches have been used for label grouping since finding all intersected label pairs is clearly infeasible for large-scale tracking problems. This paper proposes an efficient implementation of label grouping for label- partitioned generalized labeled multi-Bernoulli filter framework using a secondary partitioning technique. This allows for parallel computation in the label graph indexing step, avoiding generating and eliminating duplicate comparisons. Additionally, we compare the performance of the proposed technique with several efficient spatial searching algorithms. The results demonstrate the superior performance of the proposed approach on large-scale data sets, enabling scalable trajectory estimation. |
| first_indexed | 2025-11-14T11:46:42Z |
| format | Conference Paper |
| id | curtin-20.500.11937-96497 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:46:42Z |
| publishDate | 2023 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-964972025-01-09T06:21:14Z Label Space Partition Selection for Multi-Object Tracking Using Two-Layer Partitioning Lee, Ji Youn Shim, Changbeom Nguyen, Hoa Van Nguyen, Tran Thien Dat Choi, H. Kim, Y. Estimating the trajectories of multi-objects poses a significant challenge due to data association ambiguity, which leads to a substantial increase in computational requirements. To address such problems, a divide-and-conquer manner has been employed with parallel computation. In this strategy, distinguished objects that have unique labels are grouped based on their statistical dependencies, the intersection of predicted measurements. Several geometry approaches have been used for label grouping since finding all intersected label pairs is clearly infeasible for large-scale tracking problems. This paper proposes an efficient implementation of label grouping for label- partitioned generalized labeled multi-Bernoulli filter framework using a secondary partitioning technique. This allows for parallel computation in the label graph indexing step, avoiding generating and eliminating duplicate comparisons. Additionally, we compare the performance of the proposed technique with several efficient spatial searching algorithms. The results demonstrate the superior performance of the proposed approach on large-scale data sets, enabling scalable trajectory estimation. 2023 Conference Paper http://hdl.handle.net/20.500.11937/96497 10.1109/ICCAIS59597.2023.10382268 http://purl.org/au-research/grants/arc/LP200301507 fulltext |
| spellingShingle | Lee, Ji Youn Shim, Changbeom Nguyen, Hoa Van Nguyen, Tran Thien Dat Choi, H. Kim, Y. Label Space Partition Selection for Multi-Object Tracking Using Two-Layer Partitioning |
| title | Label Space Partition Selection for Multi-Object Tracking Using Two-Layer Partitioning |
| title_full | Label Space Partition Selection for Multi-Object Tracking Using Two-Layer Partitioning |
| title_fullStr | Label Space Partition Selection for Multi-Object Tracking Using Two-Layer Partitioning |
| title_full_unstemmed | Label Space Partition Selection for Multi-Object Tracking Using Two-Layer Partitioning |
| title_short | Label Space Partition Selection for Multi-Object Tracking Using Two-Layer Partitioning |
| title_sort | label space partition selection for multi-object tracking using two-layer partitioning |
| url | http://purl.org/au-research/grants/arc/LP200301507 http://hdl.handle.net/20.500.11937/96497 |