Multiple object tracking in unknown backgrounds with labeled random finite sets
This paper proposes an online multiple object tracker that can operate under unknown detection profile and clutter rate. In a majority of multiple object tracking applications, model parameters for background processes such as clutter and detection are unknown and vary with time; hence, the ability...
| Main Authors: | , , , |
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| Format: | Journal Article |
| Published: |
IEEE
2018
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| Online Access: | http://purl.org/au-research/grants/arc/DP160104662 http://hdl.handle.net/20.500.11937/66760 |
| _version_ | 1848761386061004800 |
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| author | Punchihewa, Y. Vo, Ba Tuong Vo, B. Kim, Du Yong |
| author_facet | Punchihewa, Y. Vo, Ba Tuong Vo, B. Kim, Du Yong |
| author_sort | Punchihewa, Y. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | This paper proposes an online multiple object tracker that can operate under unknown detection profile and clutter rate. In a majority of multiple object tracking applications, model parameters for background processes such as clutter and detection are unknown and vary with time; hence, the ability of the algorithm to adaptively learn these parameters is essential in practice. In this paper, we detail how the generalized labeled multibernoulli filter, a tractable and provably Bayes optimal multiobject tracker, can be tailored to learn clutter and detection parameters on-the-fly while tracking. Provided that these background model parameters do not fluctuate rapidly compared to the data rate, the proposed algorithm can adapt to the unknown background yielding better tracking performance. |
| first_indexed | 2025-11-14T10:30:50Z |
| format | Journal Article |
| id | curtin-20.500.11937-66760 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:30:50Z |
| publishDate | 2018 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-667602022-10-27T06:25:05Z Multiple object tracking in unknown backgrounds with labeled random finite sets Punchihewa, Y. Vo, Ba Tuong Vo, B. Kim, Du Yong This paper proposes an online multiple object tracker that can operate under unknown detection profile and clutter rate. In a majority of multiple object tracking applications, model parameters for background processes such as clutter and detection are unknown and vary with time; hence, the ability of the algorithm to adaptively learn these parameters is essential in practice. In this paper, we detail how the generalized labeled multibernoulli filter, a tractable and provably Bayes optimal multiobject tracker, can be tailored to learn clutter and detection parameters on-the-fly while tracking. Provided that these background model parameters do not fluctuate rapidly compared to the data rate, the proposed algorithm can adapt to the unknown background yielding better tracking performance. 2018 Journal Article http://hdl.handle.net/20.500.11937/66760 10.1109/TSP.2018.2821650 http://purl.org/au-research/grants/arc/DP160104662 IEEE restricted |
| spellingShingle | Punchihewa, Y. Vo, Ba Tuong Vo, B. Kim, Du Yong Multiple object tracking in unknown backgrounds with labeled random finite sets |
| title | Multiple object tracking in unknown backgrounds with labeled random finite sets |
| title_full | Multiple object tracking in unknown backgrounds with labeled random finite sets |
| title_fullStr | Multiple object tracking in unknown backgrounds with labeled random finite sets |
| title_full_unstemmed | Multiple object tracking in unknown backgrounds with labeled random finite sets |
| title_short | Multiple object tracking in unknown backgrounds with labeled random finite sets |
| title_sort | multiple object tracking in unknown backgrounds with labeled random finite sets |
| url | http://purl.org/au-research/grants/arc/DP160104662 http://hdl.handle.net/20.500.11937/66760 |