Joint detection and estimation of multiple objects from image observation
The problem of jointly detecting multiple objects and estimating their states from image observations is formulated in a Bayesian framework by modeling the collection of states as a random finite set. Analytic characterizations of the posterior distribution of this random finite set are derived for...
| Main Authors: | , , , |
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| Format: | Journal Article |
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Institute of Electrical and Electronics Engineers
2010
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| Online Access: | http://hdl.handle.net/20.500.11937/15926 |
| _version_ | 1848749028251009024 |
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| author | Vo, Ba-Ngu Vo, Ba Tuong Pham, N. Suter, D. |
| author_facet | Vo, Ba-Ngu Vo, Ba Tuong Pham, N. Suter, D. |
| author_sort | Vo, Ba-Ngu |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | The problem of jointly detecting multiple objects and estimating their states from image observations is formulated in a Bayesian framework by modeling the collection of states as a random finite set. Analytic characterizations of the posterior distribution of this random finite set are derived for various prior distributions under the assumption that the regions of the observation influenced by individual objects do not overlap. These results provide tractable means to jointly estimate the number of states and their values from image observations. As an application, we develop a multi-object filter suitable for image observations with low signal-to-noise ratio (SNR). A particle implementation of the multi-object filter is proposed and demonstrated via simulations. |
| first_indexed | 2025-11-14T07:14:25Z |
| format | Journal Article |
| id | curtin-20.500.11937-15926 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:14:25Z |
| publishDate | 2010 |
| publisher | Institute of Electrical and Electronics Engineers |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-159262017-09-13T14:08:01Z Joint detection and estimation of multiple objects from image observation Vo, Ba-Ngu Vo, Ba Tuong Pham, N. Suter, D. tracking probability hypothesis density (PHD) filtering Multi-Bernoulli images Random sets track before detect (TBD) The problem of jointly detecting multiple objects and estimating their states from image observations is formulated in a Bayesian framework by modeling the collection of states as a random finite set. Analytic characterizations of the posterior distribution of this random finite set are derived for various prior distributions under the assumption that the regions of the observation influenced by individual objects do not overlap. These results provide tractable means to jointly estimate the number of states and their values from image observations. As an application, we develop a multi-object filter suitable for image observations with low signal-to-noise ratio (SNR). A particle implementation of the multi-object filter is proposed and demonstrated via simulations. 2010 Journal Article http://hdl.handle.net/20.500.11937/15926 10.1109/TSP.2010.2050482 Institute of Electrical and Electronics Engineers restricted |
| spellingShingle | tracking probability hypothesis density (PHD) filtering Multi-Bernoulli images Random sets track before detect (TBD) Vo, Ba-Ngu Vo, Ba Tuong Pham, N. Suter, D. Joint detection and estimation of multiple objects from image observation |
| title | Joint detection and estimation of multiple objects from image observation |
| title_full | Joint detection and estimation of multiple objects from image observation |
| title_fullStr | Joint detection and estimation of multiple objects from image observation |
| title_full_unstemmed | Joint detection and estimation of multiple objects from image observation |
| title_short | Joint detection and estimation of multiple objects from image observation |
| title_sort | joint detection and estimation of multiple objects from image observation |
| topic | tracking probability hypothesis density (PHD) filtering Multi-Bernoulli images Random sets track before detect (TBD) |
| url | http://hdl.handle.net/20.500.11937/15926 |