A Solution for Large-Scale Multi-Object Tracking
A large-scale multi-object tracker based on the generalised labeled multi-Bernoulli (GLMB) filter is proposed. The algorithm is capable of tracking a very large, unknown and time-varying number of objects simultaneously, in the presence of a high number of false alarms, as well as missed detections...
| Main Authors: | , , |
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| Format: | Journal Article |
| Language: | English |
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IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
2020
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| Subjects: | |
| Online Access: | http://purl.org/au-research/grants/arc/DP160104662 http://hdl.handle.net/20.500.11937/90795 |
| _version_ | 1848765430452191232 |
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| author | Beard, Michael Vo, Ba Tuong Vo, Ba-Ngu |
| author_facet | Beard, Michael Vo, Ba Tuong Vo, Ba-Ngu |
| author_sort | Beard, Michael |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | A large-scale multi-object tracker based on the generalised labeled multi-Bernoulli (GLMB) filter is proposed. The algorithm is capable of tracking a very large, unknown and time-varying number of objects simultaneously, in the presence of a high number of false alarms, as well as missed detections and measurement origin uncertainty due to closely spaced objects. The algorithm is demonstrated on a simulated tracking scenario, where the peak number objects appearing simultaneously exceeds one million. Additionally, we introduce a new method of applying the optimal sub-pattern assignment (OSPA) metric to determine a meaningful distance between two sets of tracks. We also develop an efficient strategy for its exact computation in large-scale scenarios to evaluate the performance of the proposed tracker. |
| first_indexed | 2025-11-14T11:35:08Z |
| format | Journal Article |
| id | curtin-20.500.11937-90795 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T11:35:08Z |
| publishDate | 2020 |
| publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-907952023-04-20T06:52:55Z A Solution for Large-Scale Multi-Object Tracking Beard, Michael Vo, Ba Tuong Vo, Ba-Ngu Science & Technology Technology Engineering, Electrical & Electronic Engineering Radio frequency Target tracking Signal processing algorithms Approximation algorithms Trajectory Random finite sets generalised labeled multi-Bernoulli multi-object tracking large-scale tracking OSPA RANDOM FINITE SETS MULTITARGET TRACKING PERFORMANCE EVALUATION DISTRIBUTED FUSION BERNOULLI FILTER ALGORITHMS A large-scale multi-object tracker based on the generalised labeled multi-Bernoulli (GLMB) filter is proposed. The algorithm is capable of tracking a very large, unknown and time-varying number of objects simultaneously, in the presence of a high number of false alarms, as well as missed detections and measurement origin uncertainty due to closely spaced objects. The algorithm is demonstrated on a simulated tracking scenario, where the peak number objects appearing simultaneously exceeds one million. Additionally, we introduce a new method of applying the optimal sub-pattern assignment (OSPA) metric to determine a meaningful distance between two sets of tracks. We also develop an efficient strategy for its exact computation in large-scale scenarios to evaluate the performance of the proposed tracker. 2020 Journal Article http://hdl.handle.net/20.500.11937/90795 10.1109/TSP.2020.2986136 English http://purl.org/au-research/grants/arc/DP160104662 http://creativecommons.org/licenses/by/4.0/ IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC fulltext |
| spellingShingle | Science & Technology Technology Engineering, Electrical & Electronic Engineering Radio frequency Target tracking Signal processing algorithms Approximation algorithms Trajectory Random finite sets generalised labeled multi-Bernoulli multi-object tracking large-scale tracking OSPA RANDOM FINITE SETS MULTITARGET TRACKING PERFORMANCE EVALUATION DISTRIBUTED FUSION BERNOULLI FILTER ALGORITHMS Beard, Michael Vo, Ba Tuong Vo, Ba-Ngu A Solution for Large-Scale Multi-Object Tracking |
| title | A Solution for Large-Scale Multi-Object Tracking |
| title_full | A Solution for Large-Scale Multi-Object Tracking |
| title_fullStr | A Solution for Large-Scale Multi-Object Tracking |
| title_full_unstemmed | A Solution for Large-Scale Multi-Object Tracking |
| title_short | A Solution for Large-Scale Multi-Object Tracking |
| title_sort | solution for large-scale multi-object tracking |
| topic | Science & Technology Technology Engineering, Electrical & Electronic Engineering Radio frequency Target tracking Signal processing algorithms Approximation algorithms Trajectory Random finite sets generalised labeled multi-Bernoulli multi-object tracking large-scale tracking OSPA RANDOM FINITE SETS MULTITARGET TRACKING PERFORMANCE EVALUATION DISTRIBUTED FUSION BERNOULLI FILTER ALGORITHMS |
| url | http://purl.org/au-research/grants/arc/DP160104662 http://hdl.handle.net/20.500.11937/90795 |