GLMB tracker with partial smoothing
In this paper, we introduce a tracking algorithm based on labeled Random Finite Sets (RFS) and Rauch–Tung–Striebel (RTS) smoother via a Generalized Labeled Multi-Bernoulli (GLMB) multi-scan estimator to track multiple objects in a wide range of tracking scenarios. In the forward filtering stage, we...
| Main Authors: | , |
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| Format: | Journal Article |
| Language: | English |
| Published: |
MDPI
2019
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| Subjects: | |
| Online Access: | http://purl.org/au-research/grants/arc/DP160104662 http://hdl.handle.net/20.500.11937/91012 |
| _version_ | 1848765485599948800 |
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| author | Nguyen, Tran Thien Dat Kim, Du Yong |
| author_facet | Nguyen, Tran Thien Dat Kim, Du Yong |
| author_sort | Nguyen, Tran Thien Dat |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | In this paper, we introduce a tracking algorithm based on labeled Random Finite Sets (RFS) and Rauch–Tung–Striebel (RTS) smoother via a Generalized Labeled Multi-Bernoulli (GLMB) multi-scan estimator to track multiple objects in a wide range of tracking scenarios. In the forward filtering stage, we use the GLMB filter to generate a set of labels and the association history between labels and the measurements. In the trajectory-estimating stage, we apply a track management strategy to eliminate tracks with short lifespan compared to a threshold value. Subsequently, we apply the information of trajectories captured from the forward GLMB filtering stage to carry out standard forward filtering and RTS backward smoothing on each estimated trajectory. For the experiment, we implement the tracker with standard GLMB filter, the hybrid track-before-detect (TBD) GLMB filter, and the GLMB filter with objects spawning. The results show improvements in tracking performance for all implemented trackers given negligible extra computational effort compared to standard GLMB filters. |
| first_indexed | 2025-11-14T11:36:00Z |
| format | Journal Article |
| id | curtin-20.500.11937-91012 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T11:36:00Z |
| publishDate | 2019 |
| publisher | MDPI |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-910122023-05-12T05:03:24Z GLMB tracker with partial smoothing Nguyen, Tran Thien Dat Kim, Du Yong Science & Technology Physical Sciences Technology Chemistry, Analytical Engineering, Electrical & Electronic Instruments & Instrumentation Chemistry Engineering labeled RFS RTS smoother GLMB filter BEFORE-DETECT ALGORITHM MULTI-BERNOULLI FILTER RANDOM FINITE SETS CPHD FILTER IMPLEMENTATION TIME GLMB filter RTS smoother labeled RFS In this paper, we introduce a tracking algorithm based on labeled Random Finite Sets (RFS) and Rauch–Tung–Striebel (RTS) smoother via a Generalized Labeled Multi-Bernoulli (GLMB) multi-scan estimator to track multiple objects in a wide range of tracking scenarios. In the forward filtering stage, we use the GLMB filter to generate a set of labels and the association history between labels and the measurements. In the trajectory-estimating stage, we apply a track management strategy to eliminate tracks with short lifespan compared to a threshold value. Subsequently, we apply the information of trajectories captured from the forward GLMB filtering stage to carry out standard forward filtering and RTS backward smoothing on each estimated trajectory. For the experiment, we implement the tracker with standard GLMB filter, the hybrid track-before-detect (TBD) GLMB filter, and the GLMB filter with objects spawning. The results show improvements in tracking performance for all implemented trackers given negligible extra computational effort compared to standard GLMB filters. 2019 Journal Article http://hdl.handle.net/20.500.11937/91012 10.3390/s19204419 English http://purl.org/au-research/grants/arc/DP160104662 http://creativecommons.org/licenses/by/4.0/ MDPI fulltext |
| spellingShingle | Science & Technology Physical Sciences Technology Chemistry, Analytical Engineering, Electrical & Electronic Instruments & Instrumentation Chemistry Engineering labeled RFS RTS smoother GLMB filter BEFORE-DETECT ALGORITHM MULTI-BERNOULLI FILTER RANDOM FINITE SETS CPHD FILTER IMPLEMENTATION TIME GLMB filter RTS smoother labeled RFS Nguyen, Tran Thien Dat Kim, Du Yong GLMB tracker with partial smoothing |
| title | GLMB tracker with partial smoothing |
| title_full | GLMB tracker with partial smoothing |
| title_fullStr | GLMB tracker with partial smoothing |
| title_full_unstemmed | GLMB tracker with partial smoothing |
| title_short | GLMB tracker with partial smoothing |
| title_sort | glmb tracker with partial smoothing |
| topic | Science & Technology Physical Sciences Technology Chemistry, Analytical Engineering, Electrical & Electronic Instruments & Instrumentation Chemistry Engineering labeled RFS RTS smoother GLMB filter BEFORE-DETECT ALGORITHM MULTI-BERNOULLI FILTER RANDOM FINITE SETS CPHD FILTER IMPLEMENTATION TIME GLMB filter RTS smoother labeled RFS |
| url | http://purl.org/au-research/grants/arc/DP160104662 http://hdl.handle.net/20.500.11937/91012 |