Tracking spawning objects
Many multi-object tracking scenarios are complicated by the fact that an object of interest may spawn additional objects which, for some period of time, follow roughly the same trajectory as the original object and then fall away. The challenge is then to discriminate the original object from the sp...
| Main Authors: | , |
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| Format: | Journal Article |
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The Institution of Engineering and Technology
2013
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| Online Access: | http://hdl.handle.net/20.500.11937/56326 |
| _version_ | 1848759846359269376 |
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| author | Mahler, Ronald Maroulas, V. |
| author_facet | Mahler, Ronald Maroulas, V. |
| author_sort | Mahler, Ronald |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Many multi-object tracking scenarios are complicated by the fact that an object of interest may spawn additional objects which, for some period of time, follow roughly the same trajectory as the original object and then fall away. The challenge is then to discriminate the original object from the spawned ancillaries in a timely fashion. This study proposes a solution to this problem based on the increasingly well-known multi-object track-before-detect algorithm called the cardinalised probability hypothesis density (CPHD) filter. Precisely, the authors assume zero false alarms (ZFA) in the CPHD filter, and apply the proposed scheme to linear and non-linear simulation scenarios based on widely used object-trajectory and sensor models. The authors have also demonstrated that a Gaussian mixture implementation of the ZFA-CPHD filter (i) establishes stable estimates of object number, (ii) rapidly eliminates the ancillary objects and (iii) detects and accurately estimates the trajectory of the original object of interest. |
| first_indexed | 2025-11-14T10:06:22Z |
| format | Journal Article |
| id | curtin-20.500.11937-56326 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:06:22Z |
| publishDate | 2013 |
| publisher | The Institution of Engineering and Technology |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-563262017-09-13T16:10:18Z Tracking spawning objects Mahler, Ronald Maroulas, V. Many multi-object tracking scenarios are complicated by the fact that an object of interest may spawn additional objects which, for some period of time, follow roughly the same trajectory as the original object and then fall away. The challenge is then to discriminate the original object from the spawned ancillaries in a timely fashion. This study proposes a solution to this problem based on the increasingly well-known multi-object track-before-detect algorithm called the cardinalised probability hypothesis density (CPHD) filter. Precisely, the authors assume zero false alarms (ZFA) in the CPHD filter, and apply the proposed scheme to linear and non-linear simulation scenarios based on widely used object-trajectory and sensor models. The authors have also demonstrated that a Gaussian mixture implementation of the ZFA-CPHD filter (i) establishes stable estimates of object number, (ii) rapidly eliminates the ancillary objects and (iii) detects and accurately estimates the trajectory of the original object of interest. 2013 Journal Article http://hdl.handle.net/20.500.11937/56326 10.1049/iet-rsn.2012.0053 The Institution of Engineering and Technology restricted |
| spellingShingle | Mahler, Ronald Maroulas, V. Tracking spawning objects |
| title | Tracking spawning objects |
| title_full | Tracking spawning objects |
| title_fullStr | Tracking spawning objects |
| title_full_unstemmed | Tracking spawning objects |
| title_short | Tracking spawning objects |
| title_sort | tracking spawning objects |
| url | http://hdl.handle.net/20.500.11937/56326 |