Tracking, Identification and Classification of Random Finite Sets
This paper considers the problem of joint multiple target tracking, identification, and classification. Standard approaches tend to treat the tasks of data association, estimation, track management and classification as separate problems. This paper outlines how it is possible to formulate a unified...
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| Format: | Conference Paper |
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SPIE
2013
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| Online Access: | http://hdl.handle.net/20.500.11937/22708 |
| _version_ | 1848750945672888320 |
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| author | Vo, Ba Tuong Vo, Ba-Ngu |
| author2 | Ivan Kadar |
| author_facet | Ivan Kadar Vo, Ba Tuong Vo, Ba-Ngu |
| author_sort | Vo, Ba Tuong |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | This paper considers the problem of joint multiple target tracking, identification, and classification. Standard approaches tend to treat the tasks of data association, estimation, track management and classification as separate problems. This paper outlines how it is possible to formulate a unified a Bayesian recursion for joint tracking, identification and classification. The formulation is based on the theory of random finite sets or finite set statistics, and specifically labeled random finite sets, which results in a propagation of a multi-target posterior which contains not only target information but all available track information. Implementations are briefly discussed. Where appropriate for particular applications this method can be considered Bayes optimal. |
| first_indexed | 2025-11-14T07:44:54Z |
| format | Conference Paper |
| id | curtin-20.500.11937-22708 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:44:54Z |
| publishDate | 2013 |
| publisher | SPIE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-227082017-09-13T13:57:44Z Tracking, Identification and Classification of Random Finite Sets Vo, Ba Tuong Vo, Ba-Ngu Ivan Kadar Conjugate Prior Classification Random Sets Finite Set Statistics Identification Tracking This paper considers the problem of joint multiple target tracking, identification, and classification. Standard approaches tend to treat the tasks of data association, estimation, track management and classification as separate problems. This paper outlines how it is possible to formulate a unified a Bayesian recursion for joint tracking, identification and classification. The formulation is based on the theory of random finite sets or finite set statistics, and specifically labeled random finite sets, which results in a propagation of a multi-target posterior which contains not only target information but all available track information. Implementations are briefly discussed. Where appropriate for particular applications this method can be considered Bayes optimal. 2013 Conference Paper http://hdl.handle.net/20.500.11937/22708 10.1117/12.2015370 SPIE restricted |
| spellingShingle | Conjugate Prior Classification Random Sets Finite Set Statistics Identification Tracking Vo, Ba Tuong Vo, Ba-Ngu Tracking, Identification and Classification of Random Finite Sets |
| title | Tracking, Identification and Classification of Random Finite Sets |
| title_full | Tracking, Identification and Classification of Random Finite Sets |
| title_fullStr | Tracking, Identification and Classification of Random Finite Sets |
| title_full_unstemmed | Tracking, Identification and Classification of Random Finite Sets |
| title_short | Tracking, Identification and Classification of Random Finite Sets |
| title_sort | tracking, identification and classification of random finite sets |
| topic | Conjugate Prior Classification Random Sets Finite Set Statistics Identification Tracking |
| url | http://hdl.handle.net/20.500.11937/22708 |